HOW DOES FOOD LITERACY AFFECT FRUIT AND VEGETABLE
CONSUMPTION AMONG CANADIAN ADOLESCENTS?

by

Sophia Mattioli
BScN, Thompson Rivers University, 1999

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
NURSING

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
August 2021

© Sophia Mattioli, 2021

ii

Abstract
Background. Adolescents are losing the opportunity to observe and practice food
skills since diets are shifting from home prepared meals made from basic ingredients to a diet
comprised of ultra-processed foods, resulting in reduced consumption of minimally
processed foods such as fruits and vegetables. Evidence suggests that engaging in food
preparation during adolescence is associated with ongoing healthy dietary behaviours and food
preparation practices into adulthood, thus developing food preparation skills in adolescence may help
better support individuals to make health informed food choices. Food literacy represents the

interdependent concepts related to the facets influencing dietary practices. The objective of
the current study was to examine the association between food literacy and fruit and
vegetable consumption (FVC) among Canadian adolescents.
Methods. Guided by the food literacy framework developed by Thomas et al. (2019),
a secondary analysis was conducted on the Canadian Community Health Survey, Rapid
Response on Food Skills (Part 2) – mechanical skills and food conceptualization. The sample
population (N=790) included all survey respondents aged 12- 17 years who responded to the
survey questions that built the outcome variable: total daily fruit and vegetable consumption.
Results. When compared to respondents who report low levels of food skill,
respondents who reported high food skill levels in the ability to cook from basic ingredients
(OR 1.84, 95% CI 1.12-3.02), freeze vegetables from raw (OR 1.50, 95% CI 1.00-2.24), and
to adjust a recipe to make it healthier (OR 3.02, 95% CI 1.29-3.26) were more likely to
consume fruits and vegetables five or more times a day. Respondents who came from
households where the highest level of household educational attainment was trades had lower
odds of consuming fruits and vegetables five or more times a day compared to households
where the highest level of educational attainment was a bachelor’s degree or higher (OR

iii

0.34, 95% CI 0.14-0.81). Within the logistic regression model, significant relationships were
found between FVC and a respondent’s sex (p=0.04), perceived eating habits (p<0.001), and
highest level of household educational attainment (p=0.02).
Discussion. Significant relationships between FVC and food literacy were evident in
food skills that were multifaceted, requiring that adolescents have the ability to perform a
number of basic food skills and reflective of several food literacy attributes. The relationships
found between multifaceted food skills and adolescent FVC suggest that food literacy
attributes are interconnected and have reciprocal relationships.
Conclusion. Dietary behaviours are influenced by multiple factors. Study findings
suggest that higher levels of adolescent food literacy, as reflected in multifaceted food skills,
have the potential to positively impact their FVC. However, when societal factors are
controlled for, food skills were not found to have a significant relationship with adolescent
FVC, suggesting that factors outside of the control of the individual have the potential to
minimize the influence of individual food literacy characteristics on adolescent FVC. Future
food literacy programs should be inclusive of adolescents from all SES and should aim to
teach and evaluate food literacy attributes that build more complex food skills.

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TABLE OF CONTENTS
Abstract

ii

List of Tables

vi

List of Figures

vii

Glossary

viii

Abbreviations

x

Acknowledgements

xi

Chapter One – Introduction and Background

1

Nutritional Transition

3

Food Literacy

5

Food Literacy Framework
Relationships Between Food Literacy Categories

6
8

Chapter 2: Literature Review

10

Literature Search Methodology

10

Results
Study Characteristics
Food Literacy Themes Within the Literature
Sex
Age

12
25
30
40
46

Summary

47

Strengths and Limitations of Review

50

Conclusion

51

Chapter 3: Methods

53

Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food Conceptualization
Data Set
53
Sample Population

56

Variable Selection

57

Statistical Analysis
Descriptive Analysis
Multivariate Analysis

59
60
63

Chapter 4 – Results

65

Study Sample Characteristics

65

Differences Between Food Literacy Characteristics and Their Relationship to FVC

71

v

Individual Food Literacy Characteristics
Dietary Behaviours
Societal Food Literacy Characteristics and FVC
Predictive Model Results
Chapter 5 - Discussion

77
78
78
79
82

Individual Food Literacy Characteristics and FVC
Food Skills
Food and Nutritional Knowledge
Self-Efficacy and Confident

83
84
85
85

Dietary Behaviours and FVC

86

Societal/ External Factors and FVC
Household Income
Household Education
Geography
Sex

86
87
87
88
89

Opinion of Weight

90

Chapter 6 – Summary

92

Strengths and Limitations
Study Strengths
Study Limitations

92
92
92

Implications for Clinical Practice and Research
Food Skills
Household Education and Income
Food Literacy Framework
Data Collection of Processed/ Snack Foods
Availability of FVC
Parental Food Literacy

96
96
97
98
98
99
99

Summary and Conclusions

99

References

103

Appendix A - Copyright

115

Appendix B - Narrative Description of Literature Search

117

Appendix C - Literature Search Inclusion and Exclusion Criteria

118

Appendix D - Variables Table

119

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List of Tables
Table 1 - Summary of the NOVA Food Classification Categories.

4

Table 2 - Summary of Studies Included in Current Literature Review of the Relationship
Between Adolescent Food Literacy and Dietary Behaviours (N=28).

14

Table 3 - Food Literacy Attributes Within the Selected Studies (N=28).

24

Table 4 - Descriptive Characteristics of Study Sample, Canadians, 12 to 17 Years of Age,
Canadian Community Health Survey – Rapid Response on Food Skills (Part 2) – Mechanical
Skills and Food Conceptualization 2013 (N=790), (Weighted N=2,127,482).
66
Table 5 - Sample Population Characteristics by Reported Fruit and Vegetable Consumption
for Canadians, 12 to 17 Years of Age, Canadian Community Health Survey – Rapid
Response on Food Skills (Part 2) – Mechanical Skills and Food Conceptualization 2013
(N=790).
72
Table 6 - Logistic Regression Model Predicting Probability of Consuming Fruits and
Vegetables Five or More Times a Day for Canadians, 12 to 17 Years of Age, Canadian
Community Health Survey – Rapid Response on Food Skills (Part 2) – Mechanical Skills and
Food Conceptualization 2013 (n=674).
81

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List of Figures
Figure 1 - Food Literacy Framework.

7

Figure 2 - Population, Intervention, Effect (PIE) Formulated Question. “What is the
Relationship Between Adolescent Food Literacy and Dietary Behaviours?”

11

Figure 3 - Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)
Model of Literature Searches Conducted November 01, 2020.
12
Figure 4 - Study Variables from Canadian Community Health Survey - Rapid Response on
Food Skills (Part 2) – Mechanical Skills and Food Conceptualization Data Set That Align
With Categories From the Food Literacy Framework by Thomas et al., 2019.
59
Figure 5 - Distribution of Total Household Income From all Sources, Canadians, 12 to 17
Years of Age, Canadian Community Health Survey – Rapid Response on Food Skills (Part 2)
– Mechanical Skills and Food Conceptualization 2013 (N=790).
68
Figure 6 - Highest Level of Household Educational Attainment, Canadians, 12 to 17 Years of
Age, Canadian Community Health Survey – Rapid Response on Food Skills (Part 2) –
Mechanical Skills and Food Conceptualization 2013 (N=790).
69
Figure 7 - Sample Variables Descriptive of Individual Food Literacy Characteristics,
Canadians, 12 to 17 Years of Age Canadian Community Health Survey – Rapid Response on
Food Skills (Part 2) – Mechanical Skills and Food Conceptualization 2013 (N=790).
70
Figure 8 - Food Literacy Variables Found to be Predictive of Adolescent Consumption of
Fruits and Vegetables Five or More Times a Day, for the Sample Population, Canadians, 12
to 17 Years of Age, Canadian Community Health Survey – Rapid Response on Food Skills
(Part 2) – Mechanical Skills and Food Conceptualization 2013 (N=790).
83

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Glossary
Aboriginal Person. As defined within the Canadian Community Health Survey, “Aboriginal
people are usually those with ancestors who resided in North America prior to European
contact and who identify with one of the three Aboriginal groups listed on the questionnaire:
First Nations (North American Indian), Métis and Inuk.” (Statistics Canada, 2013c, p.73).
Although, the term “Indigenous” is increasingly replacing the term “Aboriginal”, this study
uses the language as described in the survey data set, “Aboriginal”, when referring to
Indigenous survey respondents.
Adolescence. Adolescence refers to youth in the transitional period between puberty and
adulthood, characterized by increased autonomy and independent decision making (Brooks &
Begley, 2014; Smetana et al., 2004; Vaitkeviciute et al., 2014; Wray-Lake et al., 2010).
Body Mass Index. “Body mass index (BMI) is a person’s weight in kilograms divided by the
square of height in meters. A high BMI can be an indicator of high body fatness” (Center for
Communicable Disease and Prevention [CDC], 2020).
Diet. The “habitually taken food and drink” (Diet, n.d).
Food Culture. Culture is defined as “the set of shared attitudes, values, goals, and practices
that characterizes an institution or organization” (Culture, n.d). Food culture refers to the
shared attitudes, values, goals, and practices surrounding the production and consumption of
food.
Food Literacy. Vidgen and Gallegos (2014) define food literacy as:
the scaffolding that empowers individuals, households, communities or nations to
protect diet quality through change and strengthen dietary resilience over time. It is
composed of a collection of inter-related knowledge, skills and behaviours required to
plan, manage, select, prepare and eat food to meet needs and determine intake (p. 54).

ix

Food Skills. “The knowing how, or the techniques of food purchasing, preparation, handling,
and storage” (Azevedo et al., 2017, p. 2409).
Health Literacy. “The degree to which individuals have the capacity to obtain, process and
understand basic health information and services needed to make appropriate health decisions”
(Ratzan & Parker, 2002, p.vi).
Nutritional Knowledge. Nutritional knowledge “broadly defined, refers to knowledge of
concepts and processes related to nutrition and health including knowledge of diet and health,
diet and disease, foods representing major sources of nutrients, and dietary guidelines and
recommendations” (Miller & Cassady, 2015, p.209).
Nutritional Transition. Nutritional transition is defined as, “the changes in dietary patterns
and nutrient intakes when populations adopt modern lifestyles during economic and social
development, urbanization and acculturation” (Vandevijvere et al., 2013, p. 135).
Obesogenic Food Environments. Obesogenic food environments are those food
environments that contribute to behaviours associated with obesity, and often include fast food
outlets and convenience stores, in addition to advertisements and in-store food promotions at
supermarkets for ready-to-eat food products (Timmermans et al., 2018).
Self-Efficacy. Self-efficacy “examines the construct related to health behaviour’s; the efficacy
or capacity of performance in settings or situations, including overcoming obstacles to
participate in an exercise” (Thomas et al., 2019, p.564).
Social Determinants of Health. The social determinants of health are “the social and
economic factors that influence people's health” (Canadian Public Health Association [CPHA],
2021).

x

Abbreviations
BMI – Body Mass Index
CCHS – Canadian Community Health Survey
FS2 - Rapid Response on Food Skills (Part 2)–Mechanical Skills and Food
Conceptualization
CPHA – Canadian Public Health Association
FVC – Fruit and Vegetable Consumption
FVCGTOT- Total Daily Fruit and Vegetable Consumption
SDH – Social Determinants of Health
SES – Socio-Economic Status
WHO – World Health Organization

xi

Acknowledgements
I would like to sincerely thank my supervisory committee: Dr. Caroline Sanders, Dr.
Shannon Freeman, and Dr. Erin Wilson. Your knowledge and expertise have been invaluable
to the completion of this thesis. Thank you for your constructive suggestions and intelligent
insights, which have helped me to continually re-examine my work. I would also like to
acknowledge and thank my committee and external examiner, Dr. Jonathan Alschech, for
reviewing my completed thesis over a condensed time period.
A special thank you to Larine Sluggett of the UNBC Statistics Canada Research Data
Centre, for assisting me through the Statistics Canada data use application process and
always being available to answer any questions that emerged as I conducted my data
analysis.
To my husband, Jason, thank you for tirelessly supporting me throughout this
endeavour. I would not have achieved this without you, thank you for your patience and
encouragement.

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How Does Food Literacy Affect Fruit and Vegetable Consumption Among Canadian
Adolescents?
Chapter One – Introduction and Background
Adolescents are losing the opportunity to observe and practice food skills as diets are
shifting from home prepared meals made from basic ingredients to a diet comprised of ultraprocessed foods, which require little food preparation skills (Monteiro et al., 2016; Slater &
Mudryj, 2016). Changing food practices within the home setting has resulted in less time spent
on food-related activities, thereby reducing the opportunity for adolescents to acquire important
food preparation skills (Nelson et al., 2013; Slater & Mudryj, 2016). These dietary shifts have
contributed to a general ‘de-skilling’ with respect to food choices and the preparation and
consumption of foods associated with a healthy diet. (Caraher and Lang, 1999, p.9; Jaffe and
Gertler, 2006, p. 143, 157-158; Slater & Mudryj, 2016, p. 113). Adolescent’s declining ability to
perform food skills increases their reliance on low-quality ultra-processed foods and has been
linked to reduced consumption of minimally processed foods, such as fruits and vegetables
(Caraher and Lang, 1999; Chu, et al., 2012).
The low-quality nature of ultra-processed food contributes to the rising occurrence of
chronic disease attributed to poor diet quality (Crino et al., 2015; Djupegot et al., 2017; Holman
& White, 2011; IDRC, 2011; Lam & Adams, 2017; Monteiro et al., 2013; Nagle et al., 2015;
Poirier et al., 2019; Whiteman & Wilson, 2016; World Health Organization [WHO], 2002). The
development of individual food skills that enable meals to be prepared from minimally processed
foods, such as fruits and vegetables, may improve dietary habits. Thereby, focusing on the
development of food skills could result in a diminished reliance on ultra-processed foods,
potentially reducing an individual’s risk of chronic disease attributed to low quality diet.

2

Evidence suggests that engaging in food preparation during adolescence is associated with
ongoing healthy dietary behaviours and food preparation practices into adulthood, thus
developing food preparation skills in adolescence may help better support individuals to make
health informed food choices, limiting their consumption of ultra-processed foods, and thereby
improving population health (Brooks & Begley, 2014; Laska et al., 2011; Vaitkevicicute et al.,
2014).
Evidence suggests that food literacy helps to shape adolescent eating behaviour
(Vaitkevicicute et al., 2014). Food decisions and dietary behaviours are complex interactions and
can best be understood through the lens of food literacy. Food literacy has emerged as a concept
derived from the principles of health literacy that link nutritional knowledge, food skills and diet
behaviours. These attributes have been found to positively impact dietary behaviours (Colatruglio
& Slater, 2016; Fernandez et al., 2020), and as such, initiatives aimed at improving population
health have increasingly focused on developing food literacy (Colatruglio & Slater, 2016).
Nutritional knowledge and food skills acquired earlier in childhood influence the
development of the adolescent’s self-efficacy related to food choice, with skills mastery
supporting food preparation confidence. Adolescence refers to youth in the transitional period
between puberty and adulthood, characterized by increased autonomy and independent decision
making (Brooks & Begley, 2014; Smetana et al., 2004; Vaitkeviciute et al., 2014; Wray-Lake et
al., 2010). With an increase in autonomous decision making and freedom of movement away
from the household, exploring food literacy in adolescents can aid in understanding the impact
and predictive qualities of the facets of food literacy.
This study will examine how the facets of food literacy influence fruit and vegetable
consumption (FVC) among Canadian adolescents. To critique the complexity of food literacy

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applied to food knowledge, skills, and dietary decision making, the following discussion
examines the impact that a transition to an ultra-processed diet has had on adolescent food
literacy, defines the concept of food literacy, and introduces the food literacy framework which
guides this study’s investigation.
Nutritional Transition
A number of factors contribute to the issue of increased consumption of ultra-processed
foods, and the resulting loss of food literacy in adolescents. One such factor is described as
nutritional transition, “the changes in dietary patterns and nutrient intakes when populations
adopt modern lifestyles during economic and social development, urbanization and acculturation”
(Vandevijvere et al., 2013, p. 135). Nutritional transition occurs as populations move to urban
centers, where they become distanced from their food sources, and acculturated to the most
accessible and convenient food systems (Monteiro et al., 2016). The resulting dominant food
culture emerging from nutritional transition has become one dominated by packaged, ready to
consume products (Monteiro et al., 2016, p.29). Between 1938 and 2001, Canadian’s have
increasingly purchased ultra-processed foods comparative to “unprocessed or minimally
processed foods and processed culinary ingredients” (Moubarac et al., 2017, p. 514). The reliance
on low-quality ultra-processed foods as a main component of daily dietary intake is evident
within the Canadian population, where in 2004 ultra-processed foods represented just under half
(47.7%) of daily dietary intake (Moubarac et al., 2017). Reducing the intake of ultra-processed
foods and increasing the consumption of meals made from unprocessed or minimally processed
foods is recommended to improve diet quality (Moubarac et al., 2017, p. 512).
To reduce the intake of ultra-processed foods and subsequently increase the intake of
unprocessed or minimally processed foods, it is beneficial to understand what foods fall within

4

each of these categories, and the thereby better understand the food skills required for preparation
of these foods. The term ultra-processed food is derived from the NOVA food classification
(Table 1). NOVA is an internationally recognized tool that categorises food according to the
extent and purpose of food processing, rather than in terms of nutrients (Monteiro et al., 2016).
Fruit and vegetable consumption, as indicated in Group one, is described as a natural food and a
healthier option, with no additives. As such, FVC is a reasonable variable to examine for
evidence that an adolescent is making a healthy food choice.
Table 1
Summary of the NOVA Food Classification Categories
NOVA Food Classification Categories
Group 1
Unprocessed food

Description
of food
contents

Examples

Group 2
Processed culinary
ingredients

Group 3
Group 4
Processed foods
Ultra-processed food and
made from simple
drink
ingredients
• Natural foods
• Obtained directly • Foods made by
• Contains industrial
that have been
from Group 1
adding group 1
formulations.
minimally
foods or by
and group 2
• May contain five or
processed
natural process
foods together.
more ingredients.
through the
such as grinding, • Typically contain • May include
removal of
milling, or
two or three
substances not found
unwanted parts,
pressing.
ingredients.
in culinary
drying, crushing, • Used to make
preparation.
grinding, boiling,
products to
• Minimal to no Group
roasting, or
prepare, season,
1 foods.
freezing.
and cook Group
• Products from
• Foods which
1 foodsGroups 1 and 3 that
have had nothing
contain cosmetic or
added to them.
sensory intensifying
ingredients.
The edible parts of
Salt, sugar, molasses, Cheese, smoked
Stabilizers, preservatives,
plants, fungi, water,
honey, vegetable oils, meats, and freshly
and dyes. Pasties, cereal,
and of animals after
and butter. Starches
made bread.
pre-prepared, pasta,
separation from
such as corn.
pizza, hot dogs, flavored
nature. Minimally
yogurt, and emulsifiers
processed foods.
added to breads.

Note. Adapted from “NOVA. The star shines bright. [Food classification. Public Health]” by
C.A. Monterio, et al., 2016, World Health, 7(1-3), 29-38.

5

As a high-quality food choice, fruits and vegetables can require a level of food
preparation to be included in a meal made from simple ingredients (NOVA Group three) or
prepared and stored for future use. Preparation of unprocessed foods or minimally processed
foods requires both knowledge and skill, since the individual needs to be aware of the nutritional
value of the food as well as the ways in which to prepare, cook, and store unprocessed food to
retain nutritional value and maintain food safety. While the shift towards increased consumption
of ultra-processed foods (NOVA Group four) has been thought to erode food skills, the
development of food skills enables an individual to prepare food from basic ingredients, which
has been linked to lower consumption of ultra-processed foods (Lam & Adams, 2017).
Food Literacy
The use of the phrase “food literacy” has increased rapidly since first introduced, with the
phrase being defined 39 times and cited more than 1049 times in the academic and gray literature
between 2001 and 2016 (Truman et al., 2017). The term food literacy first emerged in 2001 in a
study by Kolasa et al. (2001) that aimed to increase food and nutritional knowledge of adults with
a low-income (Truman et al., 2017) through the application of health literacy concepts to food
and nutrition. Since the concept of food literacy was first introduced, it has evolved “as a term to
describe the everyday practicalities associated with healthy eating” (Vidgen & Gallegos, 2014,
p.50). The concept is also noted across a diverse range of literature as a perspective from which
to explore the many factors, knowledge, skills, and behaviours linked to food (Azevedo Perry et
al. 2017; Truman et al., 2017; Vidgen & Gallegos, 2014). The generality of the description of
food literacy is vague and broad in its ability to guide the current study’s investigation, as such it
is necessary to further define food literacy. The current study will employ the frequently cited

6

food literacy definition by Vidgen and Gallegos (2014), which captures the emerging
perspectives, and defines food literacy as:
the scaffolding that empowers individuals, households, communities, or nations to protect
diet quality through change and strengthen dietary resilience over time. It is composed of
a collection of inter-related knowledge, skills and behaviours required to plan, manage,
select, prepare and eat food to meet needs and determine intake (p.54).
Food literacy is a concept used to situate and further explore the complexity of both
individual and social factors that influence and shape dietary patterns and behaviours at an
individual, household, community, and wider social level. Organizing food literacy into a
framework can aid in understanding the overlapping constructs that serve to inform this study.
Food Literacy Framework
When evaluating study variables for attributes of food literacy, a framework is useful, as
the aforementioned definition is broad in its ability to guide the selection of variables that align
with the underlying concepts of food literacy. A scoping review by Azevedo Perry et al. (2017)
identified attributes of food literacy, resulting in the development of a food literacy conceptual
model, which has since been developed into a proposed food literacy framework (Figure 1) aimed
at supporting the development of a food literacy measurement tool; a Locally Driven
Collaborative Projects program, funded and supported by Public Health Ontario (Thomas et al.,
2019). The food literacy framework identifies five categories: food and nutritional knowledge “facts and information acquired through experience or education related to foods and nutrition”;
food skills - “techniques of food purchasing, preparation, handling, storage”; self-efficacy and
confidence - “examines the construct related to health behaviour’s; the efficacy or capacity of
performance in settings or situations, including overcoming obstacles to participate in an

7

exercise”; societal/ ecologic - “meso- and macrosystem that interact with food decisions and
practices”; and food decisions - “applying knowledge, information, skills to make healthy food
choices” (Thomas et al., 2019, p. 564-566). Within the framework’s five categories, 11 key
attributes are identified to further define each category. The framework appears visually as a
puzzle, where food skills are linked to food and nutritional knowledge, self-efficacy and
confidence, and food decisions, which are all ultimately influenced by ecological factors. These
interdependent concepts related to the many facets of food literacy influence dietary practices and
are easily identifiable within the framework. To examine food skills among Canadian adolescents
and their relationship to FVC, all pieces of the puzzle must be considered.
Figure 1
Food Literacy Framework

Note. Reprinted from “Complexities in conceptualizing and measuring food literacy” by H.
Thomas et al., 2019, Journal of the Academy of Nutrition and Dietetics, 119(4), p. 570. Copyright
License Number 4986850126470. Copyright certificate in Appendix A.

8

Relationships Between Food Literacy Categories
Although the Thomas et al. (2019) food literacy framework places food literacy attributes
into categories, the authors note that the attributes operate in an interdependent manner (Azevedo
Perry et al., 2017; Thomas et al., 2019) and as such should not be evaluated completely
independent of each other. For example, nutritional knowledge creates the conditions for change
(Vaitkeviciute et al., 2014), but when isolated does not generate desirable dietary behaviour
without self-efficacy and food skills (Carbone & Zoellner, 2012; Guntzviller et al., 2017; Taggart
et al., 2012). While an individual may know that one should eat well, what a healthy diet
encompasses, and how to read nutritional labels, their ability to act on this knowledge may be
impacted by their ability to perform the necessary food skills to prepare healthy meals. Food
skills, beyond simple technical skills such as chopping, mixing, stirring, and measuring of
ingredients, include the ability to read recipes and plan meals (Lam & Adams, 2017), and have
been associated with higher self-efficacy related to an individual’s ability to prepare foods from
basic ingredients (Azevedo et al., 2017; Bailey et al., 2019). However, although self-efficacy can
aid in an individual’s desire to develop and perform food skills, self-efficacy alone is not enough
to change dietary behaviours. Despite an individual’s belief that they can execute healthy food
choices, they must also have behavioural capacity and health literacy to perform the desired
change (Guntzviller et al., 2017). Thus, the individual puzzle pieces of food literacy are
interconnected and together influence diet decisions.
Dietary behaviours are further impacted by the availability of healthy foods, which are
often impacted by extrinsic or social, cultural, and political environments that are beyond the
immediate control of the individual, nonmodifiable factors (Velardo, 2015, p. 386-388).
Although food skills may frame the way people consume food, the “availability of foods

9

determine in part the type and range of cooking to be applied” (Caraher & Lang, 1999, p.9). For
example, an individual may know that eating fruits and vegetables are part of a healthy diet and
have the food skills to prepare meals containing fruits and vegetables, however, may not have
access to fruits and vegetables due to income. Barriers to accessing healthy foods extend beyond
income. Food culture represents one such modifiable barrier that can be attributed to nutritional
transition within a home and thereby the food learning environment. Thus, an individual with the
economic resources to access healthy food may not possess the nutritional knowledge or food
preparation skills required to prepare foods that are minimally processed, and as such may not
provide for such foods within the home setting. Societal factors, both nonmodifiable and
modifiable, influence dietary behaviours through access to healthy food options.
When examining food skills and adolescent diet decisions through the lens of food
literacy, the development of food skills is interconnected with nutritional knowledge and selfefficacy, in addition to societal/ external attributes linked to nonmodifiable factors such as the
social processes that exist around eating behaviors, food access, (Vidgen & Gallegos, 2014), and
a changing food environment. The Thomas et al. (2019) food literacy framework offers a format
to examine the complex interconnections between the food literacy categories and or factors that
influence diet decisions, such as FVC among adolescents.

10

Chapter 2: Literature Review
A literature review was conducted to determine the current understanding of adolescent
food literacy and its relationship to dietary behaviours. The review methodology was informed by
the five stages of the Arksy and O’Malley’s (2005) methodology framework. The literature
review methodology, results, strengths and limitations, and conclusion will be discussed in the
following chapter.
Literature Search Methodology
This review was guided by the question – what is the relationship between adolescent
food literacy and dietary behaviour? The Population, Intervention, Effect (PIE) search strategy
was used to identify key search terms, Figure 2 (Hoffman et al., 2017). The population of focus
was healthy adolescents aged 12 through 17 years, however studies involving individuals outside
of these ranges were included if the study included the population of interest. Interventions were
considered if they focused the various categories and attributes encompassed in the concept of
food literacy, as outlined by Thomas et al. (2019) food literacy framework (Figure 2).
Three databases were searched May 02, 2018: Cumulative Index to Nursing and Allied
Health Literature (CINAHL) Elton B. Stephens Company host (EBSCO), PubMed Medline, and
Science Direct. The database selection was based on the data base focus, nursing, medicine, and
health care. To account for a time lapse in review analysis, the search was updated on November
01, 2020, using the search criteria outlined for the original search. Narrative descriptions of
electronic searches used in each database are outlined in Appendix B. The study selection
reporting method was based on the preferred reporting items for systematic reviews and metaanalyses (PRISMA) extension for scoping reviews (Figure 3) (Tricco et al., 2018).

11

Figure 2
Population, Intervention, Effect (PIE) Formulated Question. “What is the Relationship Between
Adolescent Food Literacy and Dietary Behaviours?”
Population
• Adolescents aged 12 to 17 years
Intervention/ Issue
• Food literacy
• Nutritional and food knowledge
• Food skills
• Self-efficacy and confidence
• Ecological or external factors
• Food or dietary decisions
Effect/ Evaluation
• Increase in attibutes of food literacy; nutrional and food knowledge, food
skills, self-efficacy and confidence
• Affect on dietary behaviours
• Outcomes resulted from data collected dirrectly from adolescents
Note. Figure 2, PIE formulated question based on “Evidence-based practice across the health
professions (3rd ed.)” by T. Hoffman et al., 2017, Elsevier.
Only English language, peer-reviewed articles which included data collected directly from
adolescents aged 12 through 17 years were included. Studies were excluded if they focused on
specific populations instead of a generalized population. Excluded study sample populations
included populations whose food behaviours were potentially impacted by significant food
security concerns, and populations who are identified as undernourished, overweight, medically
ill, or as having pre-existing conditions. As well, specific populations such as athletes, or studies
focused on a pregnant population were excluded. An expansive list of the inclusion and exclusion
criteria is included in Appendix C.

12

Figure 3
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Model of
Literature Searches, Search Dates No-Date - November 01, 2020.

Note. Search model based on “PRISMA extension for scoping reviews (PRISMA-ScR): checklist
and explanation” by A.C. Tricco, et al., 2018, Annals of Internal Medicine, 169(7), 467.
Results
The search resulted in 2,235 articles after duplicates were removed. Following application
of the inclusion and exclusion criteria to the title and abstract screen, 89 articles were retrieved
for full text review. Following full text review, 49 articles were included in the final analysis, 21

13

articles were excluded during data extraction, resulting in 28 articles being included in the
literature review.
A literature matrix was created to chart key elements from the data; categories included
authors and publication date, study purpose, study design, sample characteristics including age,
sex, country of origin, and sampling methods, key findings, and food literacy characteristics.
Table 2 provides a summary of the included studies and key findings.

"The aim of this study was to
explore what is known about
food literacy programmes
targeting adolescents, including
identification of the
constituents of food literacy
addressed, assessment of
programme effectiveness, and
description of programme
design or delivery specific to
adolescents" (p.158).
“This study aimed to gain a
better understanding of the
consumption habits of
adolescents in the school arena
by comparing different
personal characteristics and

Brooks &
Begley, 2014.

Chartatos et
al., 2018

Secondary
analysis of the
cross-sectional
study, Berlin
School
Children's
Cohort.

“Was to investigate the impact
of social living conditions and
girl’s and/or parents’ weight
status on food frequency, meal
context and daily portion sizes
among adolescent girls”
(p.1764).

Bau et al.,
2011.

"A convergent
mixed methods
design collected
qualitative and
quantitative
data in parallel.

A review of the
peer-reviewed
literature

Systematic
review

Study
Design

“To synthesise the literature on
food literacy interventions
among adolescents in
secondary schools, the
attitudes, and perceptions of
food literacy interventions in
secondary schools, and their
effects on dietary outcomes”
(p.2891).

Purpose

Bailey et al.,
2019.

Author/
Year

Sample: Quantitative n=742; qualitative
n=55
Age: Quantitative - 12-13 years and
Focus groups 12-14 years; overall mean
13.6years

Sample: 1519 and parents
Sex: 100 % female
Age: 11-14 years
Country: Germany
Sampling method: Random, from 69
East Berlin schools. Selected from the
2006-2007 Berlin School Children's
Cohort Study
Sample: 23 studies included, 19 peerreviewed papers and 4 grey literature
food literacy.
Age: 13-17.
Country: of review, Australia

Sample: 44 studies
Age: Studies with subjects aged 10-19
years of age.
Country: of review, Australia

Participant Characteristics

Students who never used the school canteen had a high self-efficacy score
(52%) (p<.001) and came from parents with a high education (65.5%)
(p<0.001), compared to those who used it often (≥ 3 times a week). Students
who often used the school canteen had higher consumption of salty snacks
(p<0.03, 21.7% vs 11.5% never), baked sweets (p=0.007, 19.6% vs 9.3% never),
and soft drinks (p=0.002, 19.6% vs 7.9% never). Students who often used the

Food literacy programs were found to improved cooking skills, food safety
knowledge, food knowledge, and short term healthy dietary behaviours. Four of
five studies on school garden programs had a positive effect on FVC. Eight of
nine studies found positive effect of food knowledge programs on dietary
behaviours. Six studies evaluated cooking and food safety programs, results
demonstrated improved cooking skills, willingness to taste new foods,
nutritional knowledge, self-efficacy, and FVC. Adolescent attitudes were
generally found to be positive towards cooking programs in school. Limited
opportunities to develop food skills was identified as a barrier to healthy eating.
A longitudinal study suggested that food skills and behaviours learned in
adolescents were sustained later in life.
Sex: “Four studies revealed that females have greater nutritional knowledge
than males.” One study identified that although both boys and girls had a
reasonable level of nutritional knowledge, only boys had good nutritional
practices” (2908).
Low family SES was associated with lower adolescent FVC compared to higher
family SES. Two parent homes were associated with higher adolescent FVC
compared to single parent homes.
Sex: Lower mothers’ education was associated negatively to adolescent eating
behaviour. Higher BMI of mothers was associated with poor adolescent eating
behaviour compared to mothers with healthy BMI. No link was found between
father’s educational or fathers’ weight and the study participants’ weight and
eating behaviour.
Nineteen studies reported positive changes related to the intervention, however
“these did not include significant changes in diet quality or cooking frequency at
home” (p.158). Programs that were successful in increasing FVC involved
interventions focused on cooking skills, some in combination with nutritional
knowledge. Key factors affecting adolescent FVC included availability of
unhealthy options in the school and home settings, the perception that health
food is expensive and peer influence. Adolescents placed value on family meals.

Key Findings

Summary of Studies Included in Current Literature Review of the Relationship Between Adolescent Food Literacy and Dietary
Behaviours (N=28).

Table 2

14

Cross-sectional,
exploratory
study.

Literature
review

"The objective of this study is
to identify the relationship
between students’ eating habits
during the school day and
sociodemographic, family and
physical activity variables, as
well as the existence of a
school cafeteria" (p.1)

“The present study is set to
recognize determinant factors
for teenagers’ nutritional
behaviours based on socialecological model” (p.1).

GarridoFernández et
al., 2020

Ghasab et al.,
2017.

Systematic
Review

Systematic
review

To conduct a “review of studies
investigating the association of
[frequency of family meals]
FFM with [nutritional status]
NS and/or [food consumption]
FC in adolescents aged 10 to 19
years" (p.2).
“To systematically review and
synthesis the literature on the
relationship between health
literacy and health behaviours
in adolescents” (p. 116).

Do Amaral e
Melo et al.,
2020

Fleary et al.,
2018.

A crosssectional
quantitative
study" (p.1)

purchasing behaviours of
infrequent and regular school
canteen users to those never or
seldom using the canteen”
(p.1).

Sample: not reported
Age: 10-19 years.
Country: of review, Iran

Sample: 8068
Sex: 50.9 %male, 49.1% female
Age: Mean age=15.71years (SD=3.65)
Country: Spain
Sampling Method: stratified random
sample, public high school students.
multistage sample stratified by clusters

Sex: Quantitative - female = 53%;
Focus groups - 29 females and 26 males,
53% female
Country: Norway
Sampling method: Quantitative – all
students in the 8th grade from 11
secondary schools. Qualitative – Staff,
plus 2 9th grade students from 6/ 11
participating secondary schools.
Sample: 50 studies
Age: 10-19, but "studies involving older
children or young adults in addition to
adolescents were also considered
eligible for
inclusion" (3).
Country: of review, Brazil
Sample: 17 studies
Age: 10 to 23 years
Country: of review, USA

“Results suggest that there is a meaningful relationship between health literacy
and adolescents’ health behavior” (p. 116). Results found that the relationship
between parental education, income, and adolescent health literacy was
consistent across the studies. Adolescents from homes with higher parental
education and income were more likely to have a higher health literacy
compared to those from homes with lower parental education. Higher health
literacy in adolescents was associated with higher health promoting behaviours.
Sex: "Of the thirteen studies examining sex, nine found no relationship between
sex and health literacy, one found a positive relationship for boys, and three
found a positive relationship for girls" (119).
The presence of a cafeteria increases the probability of not having breakfast at
home (OR:1.23), as did not regularly performing physical activity (OR:1.46)
(p<0.005). The results show that students who are 14 years old are more likely
to skip breakfast at home (p<0.001) than those 12 and 13 years of age. Students
whose mothers do not have a university education (OR:1.83), and whose both
parents do not have a university education (OR:4:48) are more likely to
consume incomplete breakfasts. Snacks with sweets (OR:1.93), candy
(OR:2.75), and bagged crisps (OR:3.06) were more likely to be consumed in
schools with a cafeteria.
Sex: Females were more likely to consume bagged crisps (OR: 1.36) and candy
(OR:1.48) during the school day than males. Females skipped breakfast more
than males (p<0.001).
Emotional eating was common in teenagers. Food preferences were often
established in childhood. Teens often have high nutritional awareness; however,
their behaviour does not always match this awareness. Mothers’ education level
and household income were positively associated with higher health behaviours.
Factors impacting nutritional behaviours included the availability of food in
home, diet restrictions applied by parents, peer influence, school rules, social
norms, food centers, media and organizational, community and public policy
determinant.
Sex: Girls had higher FVC and nutritional knowledge than boys, however, had
more unhealthy nutritional behaviours, including skipping breakfast.

Increased frequency of family meals was associated with healthier adolescent
weight, increased consumption of healthy foods (FVC, whole grains, beans,
dairy) and declining consumption of unhealthy foods (soft drinks, fried foods,
fast food). Food culture was underexplored, including food customs, and eating
practices as well as location of meal. No consensus was found regarding the
ideal number of family meals, location, type of meal, and other meal patterns.

school canteen had “higher odds of purchasing food/drink from shops near
school during school breaks and before/after school compared to the never
group (adjusted odds ratio (OR) = 1.80, 95% CI 1.07–3.01, and OR = 3.61, 95%
CI 2.17–6.01, respectively)” (p.1). Often use of school canteen was not
statistically significant for FVC (p=0.40 and 0.16).
Sex: Females represented a significantly higher proportion of students who
never used the school canteen (57.1%) (p=0.03).

15

Cross-sectional

Cross- sectional

Cross-sectional;
cross-tabulation
analyses

Cross-sectional
– Perspective
cohort study

To “measure knowledge about
nutrition and beliefs,
behaviours, and possible
barriers affecting healthy food
choices in adolescents”. The
findings were examined in
relation to eating habits and
other lifestyle factors. (p. 189)

“Examines parental report of
household food availability,
parent dietary intake and
associations with adolescent
intakes of fruits, vegetables and
dairy foods” (p. 77).

“To investigate the eating
attitudes and behaviours of
junior secondary students in
Hong Kong, with specific focus
on gender differences” (p.250).

“A longitudinal dietary survey
study provided quantitative
evidence of dietary change and
investigated factors influencing
dietary change from
adolescence to adulthood, using
sociodemographic data and
participants’ own perceptions
of, and attributions for, their
dietary change” (p. 255).

Gracey et al.,
1996.

Hanson et al.,
2004

Lai Yeung,
2010.

Lake et al.,
2004.

Sample: n=198
Sex: 81 Male and 117 Female
participants at the 20-year data
collection.
Age: 1980 mean age was 11.6 years and
2000 mean age was 32.5.
Country: United Kingdom
Sampling Method: n=405, 7th and 9th
graders, selected from 7 schools in
1980.

Sample: 902 students and 902 parents,
n=1804
Sex: Students - 424 males and 478
females, parents - targeted mothers) 810 females and 92 males
Age: Adolescent (no age stated)
Country: USA
Sampling Method:
Public middle and senior schools in
Minnesota, USA, 1998-1999.Sample
was taken from a larger Project EAT
study of 4746 participants.
Sample: n=836
Sex: 469 females, 309 males.
Age: Between 11 and 18 years
Country: Hong Kong
Sampling Method: Random sample
from 33 different secondary school
students in Hong Kong.

Sample: 391
Sex: 47% male
Age: 15-16 years, mean 15.8 years \
Country: Australia
Sampling method: All students in __
year from 3 randomly selected schools
from a list of higher and lower SES
schools as classified by the Australian
bureau of statistics, 2 public and 1
private school, from mainstream
English-speaking programs.

No significant difference was observed between respondents’ eating habits and
levels of nutritional knowledge. Adolescents chose food based on sensory
appeal and immediate food enjoyment over concern for long term health. The
impact of peer influence was minimal. 54% of respondents did not eat breakfast
and one tenth ate the recommended daily fruit and vegetable intake. 67% of
respondents believed they had adequate food knowledge. More than two-thirds
of the respondents gave correct answers to the questions in the food knowledge
quiz.
Sex: "More female students believed that their friends care about eating
healthful food (62% vs 53%), and more male students believed that their friends
care very much about staying fit and exercising than the female students (66%
vs 58%)" (p.252).
Most respondents believed that their diets became healthier over the last 20
years. Respondents believed that the increased attention given to diet and health
by the media since the 1980’s had resulted in an increased awareness of
nutrition and health positively impacting their diet choice. Participants who
believed that their parents had a positive impact on their diet had higher FVC in
adolescents. Those who felt their parents were a negative influence on their diet
had more than twice (17% increase) the increase in FVC as compared to those
who felt their parents had a positive influence (7%) over the 20 years.
Employment was cited as a reason for “time famine”; respondents who cited a
“time famine” had smaller increases in FVC over 20 years.

90.3% of parents report that fruits and vegetables are available in the home.
87% serve vegetables at dinner. 66.6% serve dairy at dinner. 56.8% had soft
drinks available in home. Availability of soft dinks in home was associated with
declined dairy intake. No significant correlations were found between serving
vegetables at supper and adolescent vegetable consumption.
Sex: Higher household availability of fruits and vegetables was associated with
increased FVC in female adolescents. Higher parental intake of dairy was
associated with higher dairy intake in adolescent males. Higher parental intake
of dairy, vegetables, and fruit was positively associated with higher female
adolescent intake of these foods.

Higher nutritional knowledge was associated with a higher variety of foods
consumed compared to those with lower nutritional knowledge. Healthy eating
declined in relation to higher alcohol and TV consumption. Adolescents who
drank alcohol or smoked cigarettes had higher fat intake compared to those who
did not. Healthy eating improved in relation to higher self-efficacy scores,
nutritional knowledge scores, considerations for weight control and well-being,
and influence over foods in the home. Barriers to healthy eating included
suitable foods within the home and school, inability to influence food choices at
home, and nutritional knowledge deficits related to fats.
Sex: Males had lower self-efficacy related to health behaviours compared to
girls, no variance when SES was controlled. Females ranked health beliefs and
values as more important compared to males. Females ranked barriers to change
higher than males. Food variety scores and female consumption of breakfast
were lower in low SES schools, compared to high SES schools.

16

Cross-sectional,
exploratory
study.

“Whether better health-related
knowledge is correlated with
favorable health behavior in
adolescents is an important,
still unanswered question. Our
objective was to examine this
relationship” (p.1).

"This research leverages theory
on goal pursuit within
relationships to investigate
whether parents are
instrumental to adolescents’
eating and activity."

Nagy-Pénzea
et al., 2020

Orehek &
Ferrer, 2018.

“Correlational”
(11), FLASHE
was a crosssectional study.

“Two-group
semiexperimental
study” (3).

“Effect of educational
intervention program for
parents on adolescents'
nutritional behaviors in Isfahan
in 2016” (p.1).

Mokhtari et
al., 2017.

Sample: n= 1556 parent and adolescent
dyads; n=3112
Sex: 50% of adolescents were female,
and 75% of parent participants were
female.
Age: Adolescent µ age was 14.45 years,
parent µ age was 43.66 years.
Country: USA
Sampling Method: Study used data
collected in the National Cancer
Institute's Family Life, Activity, Sun,
Health, and Eating study (FLASHE).
Participants to FLASHE were recruited
Ipsos Customer Opinion Panel, using
sample balancing methods.

Sample: n=63 students and unreported
parent n
Sex: 100% female and unreported
parent gender.
Age: µ=13.39 years of female student;
father µ=43.24 years; mother µ=38.15
years.
Country: Iran
Sampling Method: Random sample of
volunteers from the 7th, 8th and 9th
grade from 4 randomly drawn schools,
from 2 education districts. Participants
were divided into a control group and an
intervention group.
Sample: n=259
Sex: Male - 49.6%; female - 50.4%
Age: Mean age=14.9years, 14-16 years.
Country: Hungary
Sampling Method: All grade 9 students
in Hajdú-Bihar County, over two
academic years (2016-2017 and 20172018) were invited to voluntary
participate.

"Greater adolescent-perceived parent instrumentality was associated with
greater [FVC] and physical activity, and lower sedentariness” (p.1). Parentperceived parent instrumentality was associated with greater adolescent FVC,
less hedonic eating, and more activity. Adolescent BMI was partially associated
to parent instrumentality for activity. Sedentariness for both parent and
adolescent was associated with higher BMI. Parents’ FVC and hedonic eating
was associated with parental BMI, but not adolescent. Adolescent BMI was
associated with adolescent activity.

Adolescents with mothers who had a higher education, compared to mothers
with lower education, had lower likelihood of consuming sweets (p=0.019) and
soft drinks (p=0.029). There was no statistically significant relationship found
between higher nutritional knowledge and a healthier diet. A higher fathers’
education compared to lower education, and higher family affluence compared
to lower affluence, was associated with higher nutritional knowledge. Increasing
age was negatively associated with nutritional knowledge, however since only
grade 9 students were accessed, the older students may represent those held
back a grade or who started school later, and therefore cannot be generalized.
Sex: Females had a higher nutritional knowledge compared to males (p=0.008).
Females had lower regular consumption of breakfast as compared to males
(p=0.027).

Sex: Mothers’ purchasing habits were influenced by what their ‘children would
eat’ and family preferences. Parent’s diet growing up influenced dietary
behaviour in 47% of females, compared to 38% of males. Having children
influenced the diets of females by 49%, compared to 38% of males. Partners
influence dietary behaviours of males by 62%, compared to 59% of females.
Parents received weekly nutritional education and girls completed food
frequency and behaviour questionnaire to evaluate the impact of the parental
education. “No significant difference was in the nutritional behaviors before
and after the intervention. Hence, just educating the parents is not enough for
achieving appropriate nutritional behaviors in the adolescents” (p.2).

17

"Crosssectionally and
prospectively 2
years later"
(p.669)

Cross-sectional

“The aim of this study was to
examine the association
between indicators of family
circumstance and adolescent
dietary behaviour and change
in dietary behaviour” (p.671).

“We investigated
socioeconomic differences in
health-related behaviors among
Slovak adolescents and the
potential modification of those
differences by gender” (p.211).

"This paper explores the ability
of eButton image analysis to
capture home food preparation
behaviors/practices in a sample
of pre- and early adolescent”
(p.2).

"This study explored
adolescents' perspectives of the
importance of food literacy on

Pearson et al.,
2009.

Pitel et al.,
2013.

Raber et al.,
2018.

Ronto et al.,
2016.

"This is a
secondary
analysis of two
eButton pilot
projects" (p.1).
"This study
employed
observational
methods"(p.2).
Mixed
methodology;
quantitative

Cross-sectional

To examine “the association
between school and
neighborhood nutrition
environment and adolescent
nutritional behaviour and
weight status” (p.655).

Park et al.,
2013.

Sample: n=3264 students completed the
2 year follow up survey, representing
58% of original participants.
Sex: At baseline, 55.5% of students
were female; 84% of parents were
female
Age: Students - 12-15years, and parent 36-50 years at baseline.
Country: Australia
Sampling Method: All year 7 and 9
students from participating schools in
Melbourne Australia, in 2004-2005 were
invited to participate in an online
survey, and a follow up survey in
2006/2007.
Sample: n = 3547
Sex: 49.4% male
Age: µ 14.3 years.
Country: Slovakia
Sampling Method: Students in the 8 th
and 9th grades of randomly selected
elementary schools.
Sample: n=31
Sex: 51.6% female
Age: Study 1 - 8-13 years, study 2 - 913.
Country: USA
Sampling Method:
Respondents to study advertisement
through various mediums and from a
volunteer data base.
Sample: n= 131
Sex: 69.5% female
Age: 12-17 years

Sample: n=939,
Sex: 49.7% of participants were female
Age: µ age 12.1 years
Country: South Korea
Sampling Method: Random selection
from 15 schools in 8 school districts in
Seoul; cluster-sampling method

Adolescents ranked food and nutritional knowledge as more important than food
skills and food capacity. Most adolescents reported low confidence in food
skills. Most felt it was important to follow national guidelines, however,

The most common activity was browsing, pantry (51.6%) or fridge (71%). Less
than half of the participants demonstrated food preparation skills beyond
opening packages (41.9%) and combining 2 or more ingredients (45.2%); actual
cutting and measuring of foods were rare (<12.9% each); and 22.6% used
microwave for heat source. 51.6% witnessed food preparation by adults.
Increasing adolescent age was associated with decreased browsing, increased
food media usage, and increased food preparation and plating.

Higher household education was found to have minimal impact of adolescent
health behaviour.
Sex: Intake measured as ‘no daily intake’ for fruits and vegetables and skipping
breakfast was more prevalent as socioeconomic class declined for females
(95.2% of those with middle SES compared to 90.3% of those with high SES;
odds ratio, 2.33).

High-density supermarkets and traditional markets in school and home
neighborhoods was associated with greater likelihood of adolescent obesity. The
effect of high-density supermarkets in home neighborhoods was neutralized
with increasing household SES. School nutrition environment was not
associated with student eating habits or weight status. Fifteen percent of
adolescents skipped breakfast, increasing as adolescents aged. Healthy eating
scores declined with age.
Sex: Younger, female students who came from more affluent families, with less
screen time, or had a stay-at-home mother, had higher scores in the healthy
eating index. Females had higher fruit consumption and had higher healthy
eating scores compared to males. Females who lived in high-density food
market neighborhoods were 58% more likely to be obese than those in less
dense neighborhoods. The employment status of the mother neutralized the
neighborhood effects on healthy eating for females. Lower neighbourhood SES
was associated with increased likelihood of male obesity compared to higher
neighborhood SES.
"A higher level of maternal education was associated with positive dietary
behaviours among adolescent boys cross-sectionally and with positive dietary
change among adolescent boys and girls longitudinally” (p. 673).
Sex: Males from dual-parent homes were less likely to have low FVC
longitudinally, compared to single parent homes. Females from dual-parent
homes were less likely to snack and more likely to consume vegetables
longitudinally, compared to single parent homes. Higher maternal employment
was negatively related to female adolescent’s diet. The influence of having a
brother was negatively associated with diet, however, was associated with
increased breakfast consumption in females.

18

"This study was designed and
conducted to determine the
level of nutritional knowledge
amongst adolescents living in
Turkey…and to detect the
extent to which their diet
complies with the
Mediterranean Diet Quality
Index (KIDMED)" (p.272).
"The aims of the present study
were to: (1) describe the
prevalence and clustering
patterns of four adolescent
health behaviors (physical
activity, TV viewing time, fruit
and vegetable consumption,
dietary fat intake); (2) examine
the sociodemographic,
behavioral and parent health
behavior correlates of the
number of health risk
behaviors" (p.125).

Sahingoz &
Sanlier, 2011.

Sanchez et
al., 2007.

their dietary behaviours"
(p.550).

Australia

Cross-sectional

Not stated, but
description is
cross-sectional.

data analysis
using SPSSv22;
Qualitative
focus group
(Theoretical
saturation
reached by
group 7,
however due to
quantitative
component 15
groups
conducted).

Sample: 878 youth
Sex: 413 female, 356 male, 53.6%
female
Age: 11-15 years, mean 12.7 years
Country: USA
Sampling: Convenience sample
accessed through primary care provider,
(6 clinics and 45 providers) in San
Diego County.

Sample: 890
Sex: 464 males and 426 females
Age: 10–14 years.
Country: Turkey
Sampling Method: voluntary, from
schools in Ankara

Country: Australia
Sampling Method: Sample was
recruited via an introductory email to
health education teachers from
Southeast Queensland who had
volunteered to participate in food
literacy research, 3 schools agreed to
participate, all students were invited to
participate between 7th and 12th grade.

Adolescents consumed 33% of daily calories from fat and roughly 3 servings of
fruits and vegetables. 11.9% met recommended guidelines for fruits and
vegetables. Risk factors were clustered: 3 risk factors (not meeting
recommended physical activity, daily fat intake, and FVC) were prevalent in
25.3% of females and 14.2% of males; 2 risk behaviours (fat intake and FVC)
were prevalent 12.4% in females and 25.4% in males; 1 risk behaviour (FVC)
was prevalent 6.3% on females and 11.3 % in males.
Sex: Number of risk factors in females increased with increasing age, being
overweight, having a parent who smoked and consumed less than 5 fruits and
vegetables per day. Increasing male age was associated with reduced risk
factors. Normal weight in males was associated with increased risk factors.
Increased parent risk behaviour was associated with increased male risk
behaviours. Females who had 2 parents who never smoked and met fruit and
vegetable guidelines had fewer risk factors.

perceived that only those on strict diets should follow the guidelines and that the
growing adolescent’s body did not need to follow the guidelines. Most did not
select food or prepare food according to guidelines, as they lacked knowledge
on how to apply them. None of the aspects of food skills were ranked in the top
6 as most important in supporting them to eat healthy, these skills were viewed
as something they would need in future when they had families. Adolescents felt
they had limited opportunities to gain food skills. Most participants reported
'sort of' confidence in cooking, and mainly prepared simple dishes or
prepackaged foods. Most participants did not understand that not all information
on the internet was reliable. Most did not read food labels; main reasons being a
lack of knowledge on what to look for and labels as to complicated. Participants
felt a positive attitude towards healthy foods led to healthy food behaviours.
Sex: Senior females rated portion size knowledge as more important than males,
and males ranked ‘knowing where their food comes from’ as more important
that females.
Nutrition knowledge levels were found to be associated with nutritional habits.
KINMED scores were higher for those students who did not skip meals. As the
number of meals increased, so did the KINMED score. As the mother’s
education level rose, the KINMED scores and nutritional knowledge score rose.
Sex: No gender differences in nutritional knowledge, but there were differences
in some question, such as girls scored higher in should consume fruits and
vegetables and not consume food late at night, whereas boys scored higher on
should enjoy eating and questions re meals being beneficial with meats.

19

Cross-sectional,
secondary
analysis

Cross-sectional,
secondary
analysis

Not stated, but
description is
cross-sectional.

Secondary
analysis of
Youth'07, a
national
representative
survey (3) –
cross-sectional.

“To assess the overall degree of
formal nutritional knowledge in
adolescents across Europe. To
examine potential
sociodemographic determinants
of nutritional knowledge such
as gender, body weight status,
immigrant status and parental
education and financial level,
using data from HELENACSS” (p.2084).

“To analyse the Nutritional
Knowledge Test (NKT) using
Item Response Theory (ITR)
analysis and to assess the
construct validity of the
Nutritional Knowledge Scale
(NKTS) and its associations
with adolescent food group
consumption and nutritional
biomarkers” (419).

To assess the association
between food consumption and
nutritional knowledge and food
evaluations (p.146). To
examine the moderating role of
nutritional knowledge in the
relationship between product
evaluation and children’s
consumption of less healthy
foods (p.146)

“To examine the relationship
between family meals and
nutrition behaviors of
adolescents” (p.3).

SichertHellert et al.,
2011.

Souza Santos
et al., 2019

Tarabashkina
et al., 2016.

Utter et al.,
2013.

Sample: n= 9107
Sex: 4691 males, 4043 females
Age: Between 13-17 years
Country: New Zealand
Sampling Method: "Data were
collected as part of Youth'07, a survey
of the health and well-being of New
Zealand youth” (p.4). Survey
respondents were randomly selected

Sample: n=3215
Sex: male 47.5%, female 52.5%
Age: 12.5-17.5 years
Country: Multicentre investigation
conducted in ten European cities, 9
countries: Austria, Belgium, France,
Germany, Greece, Hungary, Italy,
Spain, and Sweden.
Sampling Method: Data from
HELENA European Statistics, collected
in 2006-2007. Random cluster sample
stratified by geographical location, age
and socio-economic level of all students
from a selection of classes from all
schools in selected cities.
Sample: 354
Sex: Youth - 53.7% female, parent80.2% female
Age: Youth -7-13 years, parents - 62.3%
36-45 years.
Country: Australia
Sampling method: Recruited from an
annual fair visited by families in south
Australia in 2011.

Sample: n=3546
Sex: Male n=1576, female n=1746.
Age: 12.5-17.9 years
Country: Multicentre investigation
conducted in ten European cities, 9
countries: Austria, Belgium, France,
Germany, Greece, Hungary, Italy,
Spain, and Sweden.
Sampling Method: Data from
HELENA European Stats collected in
2006-2007. Random cluster sample
stratified by geographical location, age
and socio-economic level of all students
from a selection of classes from all
schools in selected cities.

Family meals were shared by 60% of participant 5 or more times a week.
Frequency of family meals was positively associated with FVC, consumption of
breakfast, healthy food recall from past week, less unhealthy food consumption
and increased healthy food availability in home environment. There was no
significant relationship found between family meals and BMI.
Sex: "Students who shared meals with their families most frequently (7 or more
times per week) were most likely to be male, of younger age, and Asian
ethnicity" (p.5).

Increased consumption of fast food by children was associated to taste and
perceived social acceptability, as opposed to being perceived as fun or healthy.
Higher nutritional knowledge weakened the relationship between product
evaluations and consumption in children younger than 11 years. Parents with
higher nutritional knowledge had children who tended to consume less-healthy
foods less frequently. Although older children (11–13 years) possessed higher
nutritional knowledge, it was not associated with their dietary behaviours;
“instead, taste and perception of social acceptability were the only factors
associated with frequent consumption of less healthy products”. (148)

Approximately 60% of adolescents answered the nutritional knowledge
questionnaire correct. There was a general misconception regarding sugar,
adolescents tended to underestimate sugar content, ie) ketchup and soft drinks.
No significant correlation was found between nutritional knowledge and BMI.
Parental education level was the only SES indicator found to affect adolescent
nutritional knowledge, nutritional behaviours and BMI. Adolescents from
parents with higher education had healthy nutritional behaviours.
Sex: Females had significantly higher total nutritional knowledge scores (NKT)
scores compared with males (62% vs. 59%). The smoking status and
educational level of the mother influenced the total NKT score significantly for
males: non-smokers had a slightly higher mean score compared with smokers
(57% vs. 60%), and the total NKT score increased with the educational level of
the mother. The educational levels of both parents influenced the total NKT
score significantly for females. Total NKT score for males and females
increased significantly with the educational level of the mother (from 58 % to
64 %).
“Nutrition knowledge was positively associated with consumption of fruits
[p=0.01], cereals [p,0.001], dairy products [p,0.001], pulses, meat and eggs
[p,0.001], and fish [p,0.001], as well as with blood concentrations of vitamin C,
β-carotene, n-3 fatty acids, holo-transcobalamin, cobalamin and folate; nutrition
knowledge was negatively associated with intake of olives and avocado
[p=0.02], alcohol [p<0.001] and savoury snacks [p=0.02]” (p.419). Nutritional
knowledge increased with age (p<0.001), gender (p=0.04), maternal education
(p<0.001), and BMI (p=0.01); 74% of those with advanced nutritional
knowledge were normal weight vs 6% underweight, 15.9% overweight, and
4.1% measured as obese according to the BMI. Higher maternal education was
associated with increased nutritional knowledge (p<0.001).
Sex: Females had higher nutritional knowledge (p=0.04) than males.

20

Systematic
review

Secondary
analysis of data
collected from
the first
interview of the
National
Longitudinal
study of
Adolescent
Health – Crosssectional.

"The present
systematic review aimed to
investigate the association
between food literacy and
adolescents’ dietary
intake"(p.650).
.

“To provide national estimates
of the frequency and
determinants of adolescents’
consumption of fruits,
vegetables, and dairy foods”
(p.365), and to see whether
parental presence when
adolescents leave for, and
return from, school influences
their consumption of various
foods.

Vaitkeviciute
et al., 2014.

Videon, &
Manning,
2003.

Sample: n=18,177,
Sex: 51% male, 49% female
Age: µ age=15.9 years,
Country: USA
Sampling Method: All 7th through
12th grade students who do not live
alone or in an institutional setting in
USA, were invited to participate.
Systematic sampling method to achieve
results that were a national
representation.

from 96 schools who agreed to
participate and were randomly selected
from 389 eligible schools using a 2stage sampling design.
Sample: 13 studies included
Age: Studies with subjects aged 10-19
years of age
Country: of review, Australia
Sampling Method: Studies where food
literacy attributes were included in the
measured variables.

Three broad approaches were identified. 1. Relationship between food
knowledge and dietary intake, 6/9 studies found a positive association. 2.
Relationship between food skills and dietary intake, one study, found that
increased food skills were associated with increased food confidence and
vegetable consumption. 3. Relationship between food behaviours and dietary
intake, 2/ 4 studies found positive associations, including frequency of cooking
or engagement in food preparation increased fruit and vegetable consumption.
While cross sectional studies were found to demonstrate cooking interventions
as having a positive short-term relationship between food literacy and dietary
intake, there was a lack of longitudinal studies to verify this relationship. One
longitudinal study reviewed found adolescents who assisted in preparing dinner
were more likely to engage in food preparation behaviours 5 years later.
Sex: "Four studies revealed that females had greater food knowledge than
males” (p.655). “One study found that females had greater food knowledge, but
poorer dietary practices compared with males" (p.655). " Three studies found
that female adolescents were more involved in food-related tasks and read food
labels more frequently compared with male adolescents" (p.655).
“Adolescents with better-educated parents had better consumption patterns than
those with less-educated parents” (p.365). “Adolescents who perceived
themselves to be overweight were significantly more likely to have poor
consumption patterns” (p.365). Parental presence at the evening meal was
associated with a lower risk of poor FVC, low consumption of diary foods, and
of skipping breakfast (p.365). "The presence of a parent in the home when
adolescents left for, and returned from, school was not significantly associated
with the adolescent’s consumption of vegetables, fruits, and dairy foods”
(p.369).
Sex: "Girls and older adolescents were significantly more likely to report eating
nothing for breakfast and poor consumption of dairy foods in the previous day
than boys and younger adolescents." (p.368).

21

22

Key themes identified during data extraction were explored according to the food literacy
attributes outlined in the framework developed by Thomas et al., 2019 (Table 3). Of the 28
studies included, there were no articles that included all food literacy attributes, however the
study by Ronto et al. (2016) explored food literacy as related to every food literacy category.
Twenty-seven studies examined relationships between individual food literacy attributes and
either perceived, observed, or measured dietary behaviour. The study by Sichert-Hellert et al.
(2011) did not directly discuss dietary behaviours, however the study explored relationships
between food literacy attributes of nutritional knowledge, socio-economic status (SES), and sex.
The study also included nutritional knowledge questions that revealed misconceptions related to
sugar contents in foods, which may impact adolescent choices in dietary behaviour and as such
was found to inform the current study objectives. Individual characteristics of food literacy were
discussed in nine studies; five reported associations between increasing food literacy attributes
and healthy dietary behaviour (Bailey et al., 2019; Brooks & Begley, 2014; Gracey et al., 1996;
Souza Santos et al., 2019; Vaitlevicuiute et al., 2014), and three found no associations (Lai
Yeung, 2010; Nagy-Pénzes et al., 2020; Tarabashkina et al., 2016). Societal characteristics of
food literacy were discussed in 23 studies; all but two (Mokhtari et al., 2017; Tarabashkina et al.,
2016) reported higher SES or healthier home learning environments as being related to healthier
dietary behaviours, and one study (Pitel et al., 2013) found minimal impact on dietary behaviour.
Socio economic status was explored in 13 studies (Bau et al., 2011; Chartatos et al., 2018; Fleary
et al., 2018; Ghasab et al., 2017; Gracey et al., 1996; Nagy-Pénzes et al., 2020; Park et al., 2013;
Pearson et al., 2020; Pitel et al, 2013; Sichert-Hellert et al., 2011; Souza Santos et al., 2019;
Tarabashkina et al., 2016; Videon & Manning, 2003) and the home learning environment in 10
studies (Brooks & Begley, 2014; do Amaral e Melo et al., 2020; Garrido-Fernández et al., 2020;

23

Hanson et al., 2004; Lake et al., 2004; Mokhtari et al., 2017; Orehek & Ferrer, 2018; Sanchez et
al., 2007; Utter et al., 2013; Videon & Manning, 2003).

X

X
X
X
X

X

X

X

X

X
X

X

X

X
X

X
X

X

X

X

X

X
X

X

X

Nutritional
Literacy

X

X

X

X

Food
techniques

Food
Skills

X

X

X

X
X

X
X

Food and
Nutritional SelfEfficacy

X
X

X

X
X

Cooking
SelfEfficacy
X

Self-Efficacy and Confidence

X

X
X

X

X
X

X

X
X

X
X

X

Food
Attitudes

X

X

X

X

X

X

Food
systems

X

X
X
X

X
X
X

X
X
X
X
X

X
X
X
X
X

X
X
X
X
X
X
X

SDH

External Factors

X

X
X
X
X

X
X
X
X
X
X
X
X
X
X
X
X
X
X

X
X
X
X
X
X
X
X

Dietary
Behaviours

Food
Decisions

2019, Journal of the Academy of Nutrition and Dietetics, 119(4), 563-573.

Note. Food literacy attributes are adapted from “Complexities in conceptualizing and measuring food literacy” by H. Thomas et al.,

Videon & Manning. (2003).

Souza Santos et al., 2019
Tarabashkina et al. (2016).
Utter et al. (2013).
Vaitlevicuiute et al.. (2014).

X
X

X
X

X

X
X
X
X

X

X

X

X

Gracey et al. (1996).
Hanson et al. (2004).
Lai Yeung, W. T. (2010).
Lake et al. (2004).
Mokhtari et al. (2017).
Nagy-Pénzes et al., 2020
Orehek & Ferrer. (2018).
Park et al. (2013).
Pearson et al. (2009).
Pitel et al. (2013).
Raber et al. (2018).
Ronto et al. (2016).
Sahingoz & Sanlier. (2011).
Sanchez et al.. (2007).
Sichert-Hellert et al. (2011).

X

X

X

X

X

X

X

Bailey, et al. (2019).
Bau et al. (2011).
Brooks & Begley. (2014).
Chortatos et al., 2018
Do Amaral e Melo et al., 2020
Fleary et al. (2018).
Garrido-Fernández et al., 2018
Ghasab et al.. (2017).

Food and
Nutritional
Language

Food
knowledge

Author

Nutritional
Knowledge

Food and Nutritional Knowledge

Food Literacy Attributes Within the Selected Studies (N=28).

Table 3

24

25

Study Characteristics
The earliest study included was from 1996, and although no limits were placed on dates
during the search process, 79% of the studies (n=22/28) were published within the last decade
(2010-2020), demonstrating the increased emphasis on food literacy concepts. Global diversity
was evident within the included studies: 29% originated from Oceania (n=8/28, seven studies
from Australia and one from New Zealand), 21% from North America (n=6/28, six studies from
the United States), 29% from Europe (n=8/28, 1 each from the United Kingdom, Germany,
Slovakia, Norway, Spain, Hungary, and two representing nine European nations), 11% from the
Middle East (n=3/28, two from Iran and one from Turkey), 7% from Asia (n=2/28, one each
from Hong Kong and South Korea), and 4% from South America (n=1/28, Brazil).
Sample. Study samples included participants who ranged in age from 7 to 23 years, and
all studies included had populations between 12 and 17 years of age. Two longitudinal
perspective cohort studies were included: one study spanned two years with study participants
ranging from 12 to 17 years (Pearson et al., 2009), the second spanned 20 years and had a mean
age of 32.5 years at the 20-year follow-up (Lake et al., 2004). Nine studies collected data from
both the adolescent and the accompanying parent. The majority of parent study participants were
female, ranging from 75% to 89.8%, five studies did not report parental sex but collected data
from parents who reside with the adolescent. Primary study sample sizes ranged from 31 to
18,177 participants. A total of 10 studies had over a thousand participants within their sample, of
which six were from national databases. Data was analysed from different national databases in
the United States (n=2/6 from national data bases), New Zealand (n=1/6 from national data
bases) and Germany (n=1/6 from national data bases). The same data base was analysed in two
European studies (HELENA study, 2006-2007). Although the majority of studies included both

26

male and female participants, two studies focused only on adolescent females (Bau et al., 2011 et
al., 2011; Mokhtari et al., 2017). The majority of study participants were purposefully recruited
through high schools, representing 68% of primary studies (n=15/22 primary studies). The
remaining 32% of the studies (n=7/22 primary studies) recruited participants by convenience
sampling, such as from an annual fair or primary care providers; by random cluster samples; or
by purposeful sampling through data bases and advertising.
Study Design. Twenty studies used a cross-sectional design, two used a mixed
qualitative and quantitative design, and six were knowledge synthesis studies. Two longitudinal
perspective cohort studies collected cross sectional data from participants at two different points,
a United Kingdom study that collected data from participants aged 11 to 12, then again 20 years
following (Lake et al., 2004), and an Australian study where data was collected from participants
aged 12-13 and 14-15 years of age and again after two years (Pearson et al., 2009). Seven studies
were cross-sectional analyses of larger national studies. Most primary studies used
questionnaires or surveys to gather data (n=19/22 primary studies), two used mixed methodology
including questionnaires and focus groups (Chartatos et al., 2018; Ronto et al., 2106), and one
conducted a secondary analysis of two observational studies (Raber et al., 2013). Theoretical
underpinnings or models used in studies included the social cognitive theory (Hanson et al.,
2004; Ronto et al., 2016), social ecological model (Ghasab et al., 2017), health belief model,
theory of planned behaviour and stages of change model (Gracey et al., 1996), food-related
lifestyle model (Lai-Yeung), social psychological theory (Orehek & Ferrer, 2018), and classical
test theory and the item response theory (Souza Santos et al., 2019; Tarabashkina et al., 2016).
Three primary studies reported frameworks, including partial nominal group technique (Ronto et
al., 2016), STROBE (a checklist to Strengthen the Reporting of Observational Studies in

27

Epidemiology) (Raber et al., 2018), and the social cognitive framework (Hanson et al., 2004).
Four of the six knowledge synthesis studies applied frameworks; PRISMA (n=4/28) (Bailey et
al., 2019; do Amaral e Melo et al., 2020; Fleary et al., 2018; Vaitkeviciute et al., 2014) and
PICOS (n=1/28) (Bailey et al., 2019).
Food Literacy Variables. Food literacy is a complex emerging concept (Brooks &
Begley, 2014; Vidgen & Gallegos, 2014) and as such measurements of food literacy varied
between studies. Dietary behaviours, external factors, and nutritional knowledge were the most
common food literacy attributes measured. Dietary behaviours were a dependent variable in 21
of the 22 primary studies (Table 3). The most common food behaviour outcome measured was
the consumption of fruits and vegetables (n=16/22 primary studies) (Bau et al., 2011; Chartatos
et al., 2018; Hanson et al., 2004; Lai Yeung, 2010; Nagy-Pénzes et al., 2020; Orehek & Ferrer,
2018; Park et al., 2013; Pearson at al., 2009; Pitel et al., 2013; Raber et al., 2018; Ronto et al.,
2016; Sanchez et al., 2007; Souza Santos et al., 2019; Utter et al., 2013; Videon & Manning,
2003). Additionally, two primary studies included fruits and vegetables, but in the context of
measuring food behaviour against the national food guidelines, which included these foods (Lake
et al., 2004; Mokhtari et al., 2017). Four of the knowledge synthesis studies included dietary
behaviours as part of their inclusion criteria (do Amaral e Melo et al., 2020; Fleary et al., 2018;
Ghasab et al., 2017; Vaitkeviciute et al., 2014). The consumption of unhealthy foods, such as fast
foods, chocolate, soda, and sweets, in addition to snacking between meals, was the second most
common dietary behaviour measured (n=13/22 primary studies) (Bau et al., 2011; Chartatos et
al., 2018; Garrido-Fernández et al., 2020; Gracey et al., 1996; Nagy-Pénzes et al., 2020; Orehek
& Ferrer, 2018; Park et al., 2013; Pearson at al., 2009; Pitel et al., 2013; Raber et al., 2018; Sauza
Santos et al., 1019; Tarabashkina et al., 2016; Utter et al., 2013). The third most common dietary

28

behaviour measured was skipping meals (n=11/22 primary studies) (Bau et al., 2011; Chartatos
et al., 2020; Garrido-Fernández et al., 2020; Lai Yeung, 2010; Nagy-Pénzes et al., 2020; Park et
al., 2013; Pearson at al., 2009; Pitel et al., 2013; Sahingoz & Sanlier, 2011; Utter et al., 2013;
Videon & Manning, 2003). Dietary intake in primary studies was also measured by dairy
(n=4/22 primary studies) (Hanson et al., 2004; Park et al., 2013; Sauza Santos et al., 1019;
Videon & Manning, 2003), fat intake (n=2/22 primary studies) (Gracey et al., 1996; Sanchez et
al., 2007), portion sizes (n=2/22 primary studies) (Bau et al., 2011; Ronto et al., 2016), according
to the national nutrition guidelines (n=2/22 primary studies) (Lake et al., 2004; Mokhtari et al.,
2016) and, family meals were discussed in relation to dietary behaviours (n=2/22 primary
studies) (Bau et al., 2011; 2013; Utter et al., 2013).
External factors were discussed in 26 studies (n=26/28). Individual food literacy
attributes discussed included nutritional knowledge and food knowledge (n=9/22 primary
studies) (Gracey et al., 1996; Lai Yeung, 2010; Lake et al., 2004; Nagy-Pénzes et al., 2020;
Raber et al., 2018; Sahingoz & Sanlier, 2011; Sichert-Hellert et al., 2011; Sauza Santos et al.,
1019; Tarabashkina et al., 2016), food skills (n=3/22 primary studies) (Lai Yeung, 2010; Raber
et al., 2018; Ronto et al., 2016), and self-efficacy and confidence (including readiness to change)
(n=10/22 primary studies) (Chartatos et al., 2018; Garrido-Fernández et al., 2018; Gracey et al.,
1996; Lai Yeung, 2010; Lake et al., 2004; Orehek & Ferrer, 2018; Raber et al., 2018; Ronto et
al., 2016; Sahingoz & Sanlier, 2011; Tarabashkina et al., 2016). Risk behaviours (physical
inactivity, consumption of alcohol, cigarettes, and cannabis) and their association to food
behaviours were also discussed (n=4/22 primary studies) (Orehek & Ferrer, 2018; Pitel et al.,
2013; Sanchez et al., 2007; Sauza Santos et al., 1019).

29

Terminology and Measurement Variances in the Literature. Food literacy was
defined four times within the reviewed studies, and these studies were the only ones to cite the
term in their title. The most cited definition of food literacy was developed by Vidgen and
Gallegos (2012, 2014), describing food literacy as “a collection of inter-related knowledge, skills
and behaviours required to plan, manage, select, prepare and eat foods to meet needs and
determine food intake” (Bailey et al., 2019; Vaitkeviciute et al., 2014). The second definition
cited was an earlier definition by Vidgen and Galegos from 2011 (Brooks & Begley, 2014). The
third definition shared many of the same elements as the first but expanded the concept to
include the larger ecological attributes that exist outside the individual (Ronto et al., 2016). The
remining studies discussed varying attributes of food literacy, including nutritional knowledge,
food skills, dietary behaviour, and SES.
No study directly defined nutritional knowledge; however, several discussed their
assessment of nutritional knowledge captured within their questionnaires (Gracey et al., 1996;
Lai Yeung, 2010, Ronto et al., 2016; Sahingoz & Sanlier, 2011; Sichert-Hellert et al., 2011;
Souza Santos et al., 2019; Vaitkeviciute et al., 2014), based on national guidelines (Gracey et al.,
1996), school curriculum (Lai Yeung, 2010), or aimed at assessing knowledge related to
nutrients in common foods (Gracey et al., 1996; Souza Santos et al., 2019).
Food skills and healthy dietary behaviours were not defined as a concept, but described
within each study, usually as a component of a questionnaire (Bailey et al., 2019; Chartatos et
al., 2018; Garrido-Fernández et al., 2020; Lai Yeung, 2010; Nagy-Pénzes et al., 2020; Orehek &
Ferrer, 2018; Ronto et al., 2016; Souxa Santos et al., 2019; Vaitkeviciute et al., 2014), or by
varying outcomes such as daily or weekly consumption of different food items.

30

Socio-economic status varied in measurement and definitions: measured by household
education only (n=6/28) (Chartatos et al., 2018; Pitel et al., 2013; Raber et al., 2018; Sahingoz &
Sanlier, 2011; Sanchez et al., 2007; Videon & Manning, 2003); by maternal education only
(n=1/28) (Pearson at al., 2009); by a combination of household education and income status
(n=2/28) (Bau et al., 2011; Sichert-Hellert et al., 2011); by a combination of household education
and employment status (n=1/28) (Garrido-Fernández et al., 2020); by a combination of
household education, income or affluence level, and employment status (n=3/28) (Hanson et al.,
2004; Nagy-Pénzes et al., 2020; Souza Santos et al., 2019); based on a national register general
(Lake et al., 2004) or bureau of statistics calculation (n=3/28) (Lai Yeung, 2010, Tarabashkina et
al., 2016; Utter et al., 2013); and SES associated with the school (n=1/28) (Gracey et al., 1996).
Additionally, each measurement of education level varied. Low classifications of education
levels ranged from illiterate (Garrido-Fernández et al., 2020; Sahingoz & Sanlier, 2011) to below
high school (Chartatos et al., 2018; Pearson at al., 2009; Raber et al., 2018; Souza Santos et al.,
2019; Videon & Manning, 2003), including primary school (Garrido-Fernández et al., 2020;
Sahingoz & Sanlier, 2011; Souza Santos et al., 2019) elementary and apprentice level (NagyPénzes et al., 2020; Pitel et al., 2013). High education levels ranged from college (Raber et al.,
2018; Videon & Manning, 2003), university (Garrido-Fernández et al., 2020; Nagy-Pénzes et al.,
2020; Pearson at al., 2009; Pitel et al., 2013; Sahingoz & Sanlier, 2011; Souza Santos et al.,
2019), or postgraduate (Raber et al., 2018).
Food Literacy Themes Within the Literature
Recurring themes associated with the relationship between food literacy and adolescent
dietary behaviours include both individual and societal factors. Additionally, demographic
differences, such as sex and age, emerged as dominant themes.

31

Food and Nutritional Knowledge. Fifteen studies discussed the individual
characteristics of food and nutritional knowledge (Bailey et al., 2019; Brooks & Begley, 2014;
Fleary et al., 2018; Gracey et al., 1996: Lake et al., 2004: Lai Yeung, 2010, Mokhtari et al.,
2017; Nagy-Pénzes et al., 2020; Ronto et al., 2016: Sahingoz & Sanlier, 2011: Sichert-Hellert et
al., 2011: Souza Santos et al., 2019; Tarabashkina et al., 2016: Vaitkeviciute et al., 2014). Most
studies did not distinguish between the attributes of food and nutritional knowledge, food
knowledge, nutritional knowledge, and food and nutritional language, with the exception of
Ronto et al. (2016). Although the Ronto et al. study included all food and nutritional knowledge
attributes, this study did not measure impact of food literacy on dietary behaviour. Two
knowledge synthesis studies discussed two of the three attributes: food knowledge and
nutritional knowledge (Bailey et al., 2019; Vaitkeviciute et al., 2014).
The effect food and nutritional knowledge had on adolescent dietary behaviours was
found to be inconsistent. Increased food and nutritional knowledge were associated with healthy
dietary behaviours including lower rates of breakfast skipping (p<0.01) (Sahingoz & Sanlier,
2011), lower consumption of convenience foods (Bailey et al., 2019; Fleary et al., 2018;
Vaitkeviciute et al., 2014) such as ‘savory snacks’ (p=0.02) (Souza Santos et al., 2019),
increased FVC (Bailey et al., 2019; Fleary et al., 2018; Lake et al., 2004), increased fruit
consumption (p=0.01) (Souza Santos et al., 2019) and higher food safety practices (Bailey et al.,
2019). However, limited association between ‘food and nutritional knowledge’ and dietary
behaviour was also found in three studies (Lai Yeung, 2011; Nagy-Pénzes et al., 2020;
Tarabashkina et al., 2016). Adolescents were found to have knowledge deficits related to fat
content and to underestimate the sugar content in products such as ketchup and soft drinks
(Gracey et al., 1996; Sichert-Hellert et al., 2011 et al.). On a nutritional knowledge questionnaire,

32

60% or more adolescents answered all nutritional knowledge questions correctly except for the
questions related to fats, which scored between 34.9-51.5% (Gracey et al., 1996 et al). Several
studies found that despite some adolescents having higher nutritional knowledge scores, this
knowledge did not always translate into practice (Nagy-Pénzes et al., 2020; Ronto et al., 2016 et
al., year) and they continued to choose convenience foods and consume low levels of fruits and
vegetables (Ghasab et al., 2017; Lai Yeung, 2010; Tarabashikna). A study by Nagy-Pénzes et al.
(2020) found that factors within the home environment, such as parental education and family
affluence level, impacted dietary behaviours, finding no significant association to nutritional
knowledge.
Only one study directly discussed the attribute of food and nutrition language, finding
that most adolescents were unfamiliar with the term food literacy (Ronto et al., 2016), however,
studies that discussed nutritional knowledge often included some nutrient terminology, such as
joual for energy, carbohydrate, protein, and fiber (Gracey et al., 1996; Sichert-Hellert et al.,
2011; Souza Santos et al., 2019).
Food Skills. Food skills represented an underexplored category of food literacy, with
only seven studies investigating food skills (Bailey et al. 2019; Brooks & Begley, 2014; Lai
Yeung, 2010; Mokhtari et al., 2017; Raber et al., 2018; Ronto et al., 2016; Vaitkeviciute et al.,
2014), of which three were knowledge synthesis studies (Bailey et al., 2019; Brooks & Begley,
2014; Vaitkeviciute et al., 2014). One study reported to have asked questions related to food
safety but never discussed the responses (Mokhtari et al., 2017). Another study reported that
47% to 57% of adolescents learned cooking skills at home, however there was no discussion
related to the impact these skills had on dietary behaviour or evaluation of the degree to which
these skills were taught (Lai Yeung, 2010).

33

Food literacy programs which included interventions that taught cooking skills were
positively associated with reports of increased cooking self-efficacy (Bailey et al., 2019), FVC
(Bailey et al., 2019; Brooks & Begley, 2014; Vaitkeviciute et al., 2014), lower consumption of
unhealthy foods (Vaitkeviciute et al., 2014), and an increased willingness to taste new foods
(Bailey et al., 2019). Adolescents who participated in meal preparation skills were more likely to
engage in meal preparation five years later (Vaitlevicuite et al., 2014). Adolescents reported they
had sufficient knowledge related to food safety from their formal and informal education and
perceived themselves to have sufficient nutritional knowledge, however, were unable to translate
this knowledge into practice due to low confidence in food skills (Ronto et al., 2016). A
secondary analysis of two eButton trials further supports the assessment that adolescents possess
low confidence in food skills, reporting adolescents engaged minimally in food preparation
activities within the home setting, mainly browsing (71% fridge browsing) and preparing foods
with minimal ingredients (45.2% combing 2 plus ingredients) or opening packages (41.9%) and
cooking in the microwave (22.6%) (Raber et al., 2018).
Self-Efficacy and Confidence. Sixteen studies discussed one or more of the attributes of
self-efficacy and confidence, nine of which discussed the attribute of nutrition literacy in relation
to the reading of food labels (Bailey et al., 2019, Fleary et al., 2018; Gracey et al., 1996; Lake et
al., 2004; Lai Yeung, 2010; Mokhtari et al., 2017; Raber et al., 2018; Ronto et al., 2016;
Vaitkeviciute et al., 2014). Although adolescents reported confidence in their ability to read
nutritional labels, one study reported that adolescents frequently did not read nutritional labels as
they lacked the knowledge to understand the label or perceived the nutritional labels as too
complicated (Ronto et al., 2016). However, no association was reported between an adolescent’s

34

confidence to read food labels and dietary behaviour (Gracey et al., 1996; Vaitkeviciute et al.,
2014).
Self-efficacy related to nutritional literacy was also examined in relation to adolescents’
ability to distinguish accurate information from the media and assess its impact on their
nutritional behaviours (Fleary et al., 2018; Lake et al., 2004; Raber et al., 2018; Ronto et al.,
2016). Adolescents were reported to not understand that all information on the internet was not
reliable (Ronto et al., 2016). As the age of adolescents increased, they were reported to observe
food media more frequently (23.1% vs. 0%), however the impact of this viewing was not
assessed (Raber et al., 2018). Longitudinally, at a 20 year follow up, respondents reported media
having a positive impact on their present diet. While study respondents reported that as
adolescents they perceived very little healthy food promotion to be present in the media; the
study did not report on the impact of negative food media (Lake et al., 2004).
Food and nutritional self-efficacy, or the confidence to select and prepare food, was
discussed in three studies (Fleary et al., 2018; Gracey et al., 1996; Chartatos et al., 2018., 2019).
The ability to make dietary decisions based on food labels was associated with healthier
nutritional behaviour (Fleary et al., 2018). Adolescents who tried to eat healthy or considered
their food choices to be healthy had higher self-efficacy scores as compared to those who
reported unhealthy diets (Gracey et al., 1996). Higher self-efficacy scores in adolescents were
associated with healthier food choices compared to unhealthy food choices (p<0.05) (Gracey et
al., 1996). Additionally, higher self-efficacy scores were associated with lower use of school
canteen and increased home packed lunch as compared to adolescents with lower self-efficacy
scores (p=0.001) (Chartatos et al., 2018). Higher school canteen use was associated with
increased consumption of ‘salty snacks’ (p,0.03), ‘baked sweets’ (p=0.007) and ‘soft drinks’

35

(p=0.002) (Chartatos et al., 2018), supporting the aforementioned relationship between higher
self-efficacy and healthier food choices. Although 78% of adolescents in one study reported to
have been taught skills related to shopping, their self-efficacy to translate this knowledge to
practice was not evaluated (Lai Yeung, 2010). Cooking self-efficacy was associated positively to
programs that incorporated food skills within their interventions (Bailey et al., 2019;
Vaitkeviciute et al., 2014).
Adolescents were found to report a positive attitude towards learning to cook and about
healthy foods (Bailey et al., 2019; Brooks & Begley, 2014; Gracey et al., 1996; Lake et al. 2004;
Lai Yeung, 2010; Orehek & Ferrer, 2018; Raber et al., 2018; Tarabashkina et al., 2016).
Adolescents reported in focus groups that they felt that the school canteen could offer healthier
alternatives to aid in the promotion of a healthy diet (Chartatos et al., 2018). Adolescents
perceived that a healthy attitude towards diet would result in a healthy diet (Ronto et al., 2016),
viewing FVC as healthy and processed foods as unhealthy (Bailey et al., 2019). Adolescents who
tried to eat healthy had lower dietary intake of fats (mean 5.3, CI 5.0-5.6) compared to those who
did not try to eat healthy (mean 5.8, CI 5.4-6.2) (Gracey et al., 1996), however it was also
reported that adolescents reported their dietary decisions to be largely influenced by taste
(p<0.05) (Tarabashkina et al., 2016) and perception of social acceptability (p<0.01)
(Tarabashkina et al., 2016). Adolescents valued social eating experiences, including family
meals (Ronto et al., 2016; Utter et al., 2013).
Although adolescents placed value on healthy eating and food skills, they often did not
apply these values to their dietary practices due to their beliefs and attitudes towards what was
important in adolescents. For example, while adolescents felt it was important to follow national
guidelines, they often did not put this value into action due to a belief that only those on strict

36

diets needed to do so, and their belief that the growing adolescent body did not need to follow
such guidelines (Ronto et al., 2016). Additionally, although adolescents viewed food skills to be
beneficial over the course of their lives, they may not view food skills to be important in their
adolescent years, but rather as skills they would need when they had families of their own (Ronto
et al., 2016).
Societal/ External Ecological Factors. Few studies examined food systems. Adolescents
were found to have limited macro level knowledge regarding their food systems, however when
introduced to the concept, males ranked the importance of ‘knowing where their food came
from’ (mean 1.9) higher than females (mean 0.5) (p≤0.05) (Ronto et al., 2016).
Changes within the food system have promoted a high density of supermarkets,
traditional markets, and fast-food outlets in lower SES neighborhoods, creating obesogenic
environments that have been found to negatively impact the consumption patterns of adolescents
who reside within these neighborhoods (Burgoine et al., 2016; Townshend & Lake, 2017). The
effect of obesogenic environments on adolescents was evident in the Park et al., (2013) study,
which found that adolescents living in neighborhoods with a high density of fast-food outlets
were more likely to be obese, as compared to those living in less dense neighborhoods, however
although the adolescents healthy eating index scores were lower for those who resided in high
density neighborhoods comparatively to those in less dense neighbourhoods, the scores were not
statistically significant (Park et al., 2013). The food system was also represented by the presence
of a school cafeterias/ canteens and neighbouring shops within school neighborhoods (Chartots
et al., 2018; Garrido-Fernández et al., 2020). ‘Often’ use of the school canteen was associated
with higher intake of unhealthy snacks compared to the adolescents who ‘never’ used the
canteen, as evident in the consumption of soft drinks where 19.6% of adolescents who ‘often’

37

visited the school canteen regularly consumed soft drinks compared to 7.9% of those who
‘never’ visited the canteen (Chartatos et al., 2018). Additionally, adolescents from schools with a
cafeteria were more likely to consume snacks with sweets (OR 1.93, CI 1.67-2.23), candy (OR
2.75, CI 2.39-3.19), and bagged crisps (OR 3.6, CI 2.65-3.54) (Garrido-Fernández et al., 2020).
Although the number of shops in the school neighbourhood did not statistical impact the overall
number of purchase (p=0.09), when there were fewer than three shops nearby, those adolescents
who visited the canteen ‘often’ made purchase from shops 39.8% of the time, and when there
were three or more shops nearby these adolescents’ purchases increased to 60.2% (Chartatos et
al., 2018).
Measurements of SES were inconsistent across the studies, comprised of varying
combinations of household education measurements, income, and SES indicators. Socioeconomic status was frequently measured in the included studies (n =15/28), with several studies
collecting socio-economic variables but never reporting the related data in relation food literacy
attributes and diet behaviour (Hanson et al., 2004; Mokhtari et al., 2017; Raber, 2018).
Despite varying measurements of SES, higher SES was generally associated with
healthier dietary behaviours among adolescents. The studies that measured and discussed
household income reported positive associations between higher household income, diet
behaviours and/ or weights status (Fleary et al., 2018; Ghasab et al., 2017; Gracey et al., 1996;
Park et al., 2013; Souza Santos et al., 2019). Students from schools with lower SES reported
skipping breakfast more frequently (p=0.014) and had lower food variety scores (p=0.034)
(Gracey et al., 1996). Female adolescents in higher SES schools had lower consumption of soft
drinks (p=0.0005) as compared to those in lower SES schools, and higher consumption of water
(p=0.031) (Gracey et al., 1996). Adolescents who came from higher SES schools or higher

38

income homes had healthier nutrition behaviours (Ghasab et al., 2017; Gracey et al., 1996) and
higher levels of health literacy. High levels of health literacy were found to have positive
associations with health behaviours, including higher FVC and lower smoking and alcohol usage
(Fleary et al., 2018). Adolescents who smoke (p=0.041) or drink alcohol (p=0.012) had higher
levels of dietary fat than adolescents that did not smoke or drink (Gracey et al., 1996).
Twelve of the 13 studies that measured SES by level of parental education found that as
the level of parental education increased, adolescent nutritional knowledge increased (NagyPénzes et al., 2020; Sahingoz & Sanlier, 2011; Sichert-Hellert et al., 2011; Souza Santos et al.,
2019). High maternal education as compared to low maternal education was associated with
higher adolescent nutritional knowledge (p<0.001) (Souza Santos et al., 2019), healthy weight
status increased (Bau et al., 2011; Souza Santos et al., 2019), and healthier dietary behaviour
increased (p<0.05) (Sahingoz & Sanlier, 2011) (Bau et al., 2011; Chartatos et al., 2018; GarridoFernández et al., 2019; Ghasab et al., 2017; Fleary et al., 2018; Nagy-Pénzes et al., 2020;
Pearson at al., 2009; Pitel et al., 2013). The higher the level of parental education, the lower the
frequency of adolescents breakfast skipping (p<0.05 – Videon & Manning, 2003) (p<0.01 – Pitel
et al., 2013), the lower the likelihood of adolescents consuming low levels of fruits and
vegetables daily (p<0.001) (Videon & Manning, 2003), and the lower the consumption of
convenience foods by adolescents (college educated compared to high school educated, p<0.05)
(Videon & Manning, 2003). A study that measured SES through a combination of parental
education and employment status found that the odds of female adolescents having a food
frequency score below the 25th percentile were higher among females who came from lower SES
compared to those from high SES (OR 3-6 , CI 2.1-6.0, p<0.001) (Bau et al., 2011, p.1760,

39

1763). Adolescents from households with parents who had a higher education had higher health
literacy, which was associated with higher FVC (Fleary et al., 2018 et al).
Opportunities for learning and practicing food literacy include family meals, availability
of healthy foods, and household composition which may affect meal preparation time, and
parental modeling. Adolescents who participate in an increasing number of family meals per
week were found to be more likely to consume five or more fruits and vegetables per day
(p<0.001) (Utter et al., 2013), 19% to 38% less likely to report poor vegetable consumption
(Videon & Manning, 2003), more likely to always consume of breakfast (p<0.001) (Utter et al.,
2013), and consumed unhealthy foods less frequently in the past week (p<0.001) (Utter et al.,
2013). The dietary benefits associated with increasing frequency of family meals listed above,
including increased consumption of healthy foods and declining consumption of unhealthy foods
are supported in a knowledge synthesis study by do Amaral e Melo et al. (2020). No significant
correlations were found between serving vegetables at supper and adolescent FVC, however
serving dairy was associated with increased dairy intake in males (p<0.01) (Hanson et al., 2004).
The most commonly reported barrier to healthy eating by adolescents, was the
availability of healthy options within the home, and or school environments (Brooks & Begley,
2014; Ghasab et al, 2017; Gracey et al., 1996; Hanson et al., 2004; Utter et al., 2013). The
availability of foods within the home environment impacted the types of foods consumed
(Brooks & Begley, 2014; Ghasab et al., 2017; Gracey et al., 1996), with 45.1% of adolescents
reporting the lack of healthy foods in the home as a barrier to healthy eating (Gracey et al.,
1996). Several studies support the impact that food availability can have on adolescent dietary
consumption. Female adolescents who resided in homes where soft drinks were available
‘always’ or ‘usually or sometimes’ compared to ‘never’, consumed approximately one fewer

40

servings of dairy per day (Hanson et al., 2004). The influence of the home food environment is
further evident in another study that found as the availability of fruits and vegetables within the
home increased the frequency of family meals increased; 96.5% of homes that reported the
frequency of family meals as seven or more times a week also usually had fruits and vegetables
available in the home, compared to 85.7% of those who reported frequency of family meals as
fewer than two times a week (OR 4.69, CI 3.7-5.9, p<0.001) (Utter et al., 2013).
Household demographics were associated with adolescent food literacy. Female
adolescents from two parent households had higher FVC (Bau et al., 2011). Utter et al. (2013)
showed an increase in frequency of family meals for adolescents who lived with two parents
verse one; meals eaten five to six times a week occurred 24.5% in two parent homes compared to
21.4% in single parent homes; and meals seven times a week occurred 36.4% in two parent
homes compared to 31.2% in single parent homes.
Sex
Sex emerged as a key theme within the review. Male and female study participants were
found to display differing individual food literacy attributes and dietary behaviours, in addition
to experiencing varying impacts from societal factors, such as SES. Females were found to score
higher in nutritional knowledge than males in seven out of eight studies that discussed or
reported sex differences (Bailey et al., 2019; Ghasab et al., 2017; Gracey et al., 1996; NagyPénzes et al., 2020; Sichert-Hellert et al., 2011; Souza Santos et al., 2019; Vaitkeviciute et al.,
2014). In knowledge test scores females had a mean score of 5.3 compared to males 4.8
(p=0.011) (Gracey et al., 1996) and were found to have slightly higher nutritional knowledge
scores than males in the Helena Studys (p<.0001 – p=0.04) (Sichert-Hellert et al., 2011; Souza

41

Santos et al., 2019). However, a study by Sahingoz & Sanlier (2011) found no significant
differences in nutritional knowledge by sex.
Females were also found to have higher self-efficacy scores in relation to food and food
related activities (Gracey et al., 1996), such as food preparation and reading food labels (Ghasab
et al., 2017, Vaitkeviciute et al., 2014). A study that measured attitudes and values attributed to
food, found the male adolescents place a significant higher value on the benefits of four types of
meats compared to females (p values ranged between 0.01 - 0.0001) (Sahingoz & Sanlier, 2011,
p.274). Whereas females were found to attribute a higher value towards FVC than males;
females scored higher on questions such as “should consume fruits” (p=0.007) and “vegetables”
(p=0.003) as compared to males (Sahingoz & Sanlier, 2011, p. 274). Additionally, a mixed
methodology study found females between 15 and 17 years of age were more concerned about
portion size than their male counterparts (p≤0.05) (Ronto et al., 2016). Another study reported
female diet decisions to be more impacted by weight control and appearance than their male
counterparts, 85% of females’ verses 67% of males kept watch of their weight (p<.001) (Lai
Yeung, 2010). These findings highlight that value attributed to portion sizes, fruits and
vegetables, and higher FVC should be viewed cautiously as these values and behaviours may be
more related to concern over weight status and may not be representative of higher food literacy.
When measuring dietary behaviour through FVC, females frequently consumed higher
levels than their male counterparts (Bailey et al., 2019; Ghasab et al., 2013; Park et al., 2013;
Ronto et al., 2016; Sahingoz & Sanlier, 2011); females consumed higher levels of vegetables
(p≤0.001) and fruits (p≤0.05) as compared to males (Pearson at al., 2009). Contradictory to study
results reporting females to have higher FVC, one study found that 25.3% of females had a
combination of all three risk factors associated with unhealthy weight (categorize as physical

42

inactivity, low FVC and unhealthy fat intake) compared to 14.2% of males (Sanchez et al.,
2007). However, when measuring for only one risk factor - low FVC - females had a lower risk
at 6.3% compared to males at 11.3% (Sanchez et al., 2007). Males were also found to have
significantly higher fat consumption scores as compared to females (mean score 6 verses 5.4,
p<0.0001); SES had no significant effect of the consumption scores (Gracey et al., 1996).
Although females are reported to have higher nutritional knowledge and self-efficacy,
these attributes did not necessarily translate into healthier dietary behaviours. In a longitudinal
study females and males demonstrated diet change in different areas; females demonstrated an
increasing rate of breakfast skipping as compared to males (13.7% verses 10.2%, p≤0.01), while
males increased snacking (16% verses 7.8%, p≤0.001) and their consumption of fast foods
(26.2% verses 15.3%, p≤0.001) as compared to females (Pearson at al., 2009). Consistent with
the knowledge synthesis studies (Ghasab et al., 2013; Vaitkeviciute et al., 2014), primary studies
within this review found that female adolescents skipped breakfast more frequently than males
(p<0.001) (Videon & Manning, 2003); 34.6% (Pearson et al., 2009) to 40.7% (Park et al., 2013)
of females skipped breakfast most days compared to 23.9% (Pearson et al., 2009) to 31% (Park
et al., 2013) of males.
Socio-economic status was found to impact the dietary behaviours of females and males
at different rates. While only 43.4% of females at low SES schools ate breakfast regularly,
compared to 73.0% of females from high SES schools regularly consumed breakfast (ꭓ2 19.16,
p=0.0140), male breakfast consumption between SES schools showed no statistically significant
difference, ranging from 72.5% to 74.0% (Gracey et al., 1996). Another study reported similar
findings, low SES compared to high SES among female adolescents was correlated to a higher

43

likelihood of no daily breakfast consumption (OR 1.50, CI 1.07-2.09, p=0.038), and no daily
fruit (OR 2.05, CI 1.40-3.00, p<0.001) (Pitel et al., 2013).
Socio-economic status was frequently measured by either highest level of household
education (n-8/28) (Bau et al., 2011; Hanson et al., 2004; Pitel et al., 2013; Sahingoz & Saniler,
2011; Sanchez et al., 2007; Sichert-Hellert et al., 2011; Videon et al., 2003) or maternal
educational (n=1/28) (Pearson et al., 2009). Higher household education was associated with a
lower likelihood of poor female dietary behaviour; no breakfast (p=0.038), no daily fruit
(p<0.001), and no daily vegetables (p=0.001), however no significant relationships were noted
related to SES and breakfast or fruit consumption among male adolescents (Pitel et al., 2013).
Interestingly, although a study found female nutritional knowledge scores to increase as
education level increased for both the father (60% to 64%, p<0.0069) and mother (58% to 64%,
p<0.0128) (Sichert-Hellert et al., 2011), another found only maternal education to impact female
nutritional knowledge scores (Bau et al., 2011). Higher maternal education was also found to be
associated with healthier adolescent dietary behaviour’s (measured by KIDMED) and a higher
nutritional knowledge score for both males and females (p<0.05), however, the father’s
education was found to have no significant impact (Sahingoz & Sanlier, 2011).
Overall, female adolescents experienced a higher influence on their behaviour from their
parents, in particular their mother. One study found that sex differences had greater influence
than any other categorical influences on health behaviour, with “unfavorable food frequency”
scores, food frequency scores in the lower 25th percentile, higher for adolescent females from
homes where the mother had less than grade 10 (OR 3.3, CI 1.7-6.2, p<0.001) compared to those
with grade 10 high school-leaving certificate, however no significant relationship was found

44

between the fathers education and female adolescent food frequency scores (Bau et al., 2011,
p.1763).
Maternal employment was also found to negatively impact female adolescent dietary
behaviour. Females who had mothers not in paid employment were more likely to decrease their
snacking over a 2-year period (p≤0.05) and were less likely to increase their fast-food
consumption (p≤0.05) as compared to those who had mothers in full time employment (Pearson
at al., 2009). Additionally, in another study the presence of a mother at home improved healthy
eating index scores for females (p<0.01) but had no statistical impact on males (Park et al.,
2013). Although females living in neighborhoods with a high density of supermarkets and fastfood outlets were more likely to be obese compared to females living in less dense
neighborhoods (OR 1.03, CI 1.01-1.05, p<0.01), the maternal employment status (not working
outside the home) neutralized the effect high density neighborhoods had on females, resulting in
a higher healthy eating index score (p<0.01), however there was no statistically significant affect
for males (Park et al., 2013). Instead, males in the same neighborhoods likelihood of being obese
was linked to the economic status of the neighborhood; males in low SES neighborhoods had
higher odds of obesity compared to males living in higher SES neighborhoods (OR 1.37, CI
1.06-1.76, p<0.01) (Park et al., 2013).
Household composition influenced the dietary behaviours of males and females
differently. Males who came from dual parent homes were less likely to consume low quantities
of fruits and vegetables (p≤0.05), whereas females were more likely to consume higher
quantities of fruits and vegetables and snack less frequently (Pearson at al., 2009). The presence
of a brother at home impacted the eating habits of both males and females; males were more

45

likely to have low consumption of vegetables (p≤0.05), and females consumed breakfast more
often (p≤0.05) (Pearson at al., 2009).
Barriers within the home and school environment were more frequently reported by
females than by males, such as the lack of availability to healthy foods within the home and
school setting (Ghasab et al., 2017; Gracey et al., 1996). When fruits and vegetables were
‘always available’ in the home, females were found to have higher FVC (median of 3.99) as
compared to households where fruits and vegetables were ‘sometimes or never available’
(median 2.69) (p<0.01), however no statistical significance relationships were noted between
males and home food environment (Hanson et al., 2004).
Household dietary behaviours impact females and males differently over the life span. A
20-year longitudinal study reported sex differences attributed to factors influencing dietary
change (Lake et al., 2004). Both males (62%) and females (59%) identified their partners to
significantly influence their diet, however 41% of males identified their partners as a positive
influence compared to 29% of females (p=0.015), with only 3% of males identifying their
partners as a negative influence compared to 15% of females (p=0.015) (Park et al., 2004).
Although no statistical significance was found between sexes attributed to the influence that their
parents had on their present dietary behaviours, 47% of females perceived their parental dietary
habits in their youth to continue to impact their nutritional behaviours 20 years later (32%
positively), compared to compared to 38% of males (27% positively) (Lake et al., 2004).
Additionally, in a cross-sectional study on adolescent’s parental dietary behaviour impacted
males and females differently (Hanson et al., 2004). Parental consumption of four or more
servings a day of fruits and vegetables compared to those who consumed less than one, was
associated with increased FVC in females (p<0.01) but had no statistical impact on males

46

(Hanson et al., 2004). However, higher parental consumption of dairy, four or more servings per
day compared to less than one, increased dairy consumption in males (p<0.01), but had no
statistical impact on the intake of females (Hanson et al., 2004).
Studies referred to study participants as boys or girls, male or female, mothers or fathers,
and gender identity was not identified or discussed. However, it should be noted that “the
language related to gender identity and sexual orientation has …evolved rapidly” (American
Psychological Association [APA], 2020, p.138), and since the majority of the included studies
were published prior to the APA 7th edition, no study used bias free language. It is not evident if
those that identified as male or female, boys or girls, reported their sex assigned at birth or
current sexual orientation. The terms male and female, and not boys or girls, are used to refer to
sex in the present study, however the complexity related to these terms as mentioned above
remains, as does the potential for bias.
Age
Food skills have been found to increase with age; a secondary analysis of two observation
studies found that the older group of participants as compared to the younger group used food
media more (23.1% vs 0%), practiced more plating (46.2% vs 16.7%), and showed more food
preparation behaviours, such as washing and peeling produce (30.8% vs 0%) and measuring
ingredients (23.1% vs 5.6%) (Raber et al., 2018 et al.). Additionally, nutritional knowledge in
adolescents was found to grow by 2% per year, by age of respondents in a cross-sectional study
(Sichert-Hellert et al., 2011). However, despite these positive changes with increasing age,
adolescents were also noted to participate less frequently in family meals (Utter et al., 2013) and
skip breakfast more often (p<0.001) (Videon & Manning, 2003). The impact of increasing age is
difficult to study due to the lack of longitudinal studies. This literature review only found two

47

studies that were longitudinal in nature, both were prospective cohort study, and discussed
changes in dietary behaviour in participants at two different ages, one on 20 years (Lake et al.,
2004) and one in two years (Pearson et al., 2009).
Summary
Themes within the literature in relation to sex, age, individual food literacy attributes and
SES. Food literacy is a relatively new concept, with the definition identified to guide this review
first developed in 2014 (Vidgen & Gallegos, 2014) and the guiding framework in 2017 (Azevedo
Perry et al., 2017), later revised in 2019 (Thomas et al., 2019). Since this review encompasses
articles dating back to 1996, it is not surprising that the vast majority of articles do not include all
food literacy characteristics, and the definitions of food literacy and measurements of each
attribute differ. As a collection of interrelated and multifaceted characteristics (Thomas et al.,
2019), few studies, including more recent studies, were able to encompass the full complexity of
food literacy, making it difficult to directly associate food literacy with dietary behaviour.
Additionally, the wider contextual factors captured in the ecological attributes of food literacy
vary in measurement and definition between the studies. Despite the challenges associated with
the lack of consistency between definitions and measurements, the individual characteristics and
attributes that comprise food literacy have been shown to impact adolescent dietary behaviour.
Programs successful in changing dietary behaviours and increasing self-efficacy and
confidence incorporated food skills into their interventions (Bailey et al., 2019; Brooks &
Begley, 2014; Vaitkeviciute et al., 2014). Most studies measured confidence from the
perspectives of the adolescent, whether they felt they had sufficient knowledge or skill in a
specific area (Lai Yeoung, 2010; Ronto et al., 2016), compared to an assessment of food literacy
related skills. Performance is difficult to measure from cross-sectional questionnaires, as they

48

were often conducted in convenient settings such as schools and health clinics. A gap identified
in this review is the absence of studies that assess the ability of an adolescent to perform food
skills, and how this performance related to dietary behaviour. Future studies may benefit from
evaluating food literacy attributes pre- and post-intervention and incorporating assessments of
activities such as cooking and shopping. Considering programs that include food skills within
their intervention report a higher success rate in changing behaviour, the investigation into the
impact food skills have on dietary behaviours warrant further exploration.
Programs where interventions aimed to increase nutritional knowledge or to evaluate the
impact of nutritional knowledge on dietary behaviours resulted in inconsistent dietary change.
Nutritional knowledge levels were impacted by SES, measured by household education levels
and household income, adolescents from homes with higher SES exhibited greater nutritional
knowledge (Fleary et al., 2018; Sahingoz & Sanlier, 2011; Sichert-Hellert, 2011) and healthier
dietary behaviour (Bau et al., 2011; Fleary et al., 2018; Ghasab et al., 2017; Gracey et al., 1996;
Park et al., 2013; Pearson et al., 2009; Pitel et al., 2013; Videon & Manning, 2003). The
evidence is not clear whether nutritional knowledge or SES had a greater impact on dietary
behaviours.
Socio-economic status and learning environments had a significant impact on the dietary
behaviours of adolescents. Higher SES measured by education or income impacted dietary
behaviour positively (Bau et al., 2011; Fleary et al., 2018; Ghasab et al., 2017; Gracey et al.,
1996; Nagy-Pénzes et al., 2020; Park et al., 2013; Pearson et al., 2009; Pitel et al., 2013; Videon
& Manning, 2003). The most cited barrier to healthy eating was the availability of healthy food
options within the home or school setting (Ghasab et al., 2017; Gracey et al., 1996; Hanson et al.,
2004). The availability of healthy and unhealthy foods within the home or neighbourhood

49

impacted adolescent dietary behaviours (Park et al., 2013), as when unhealthy foods were
available, they were chosen over healthy options. Additionally, as SES declined, other risk
behaviours increased and were generally associated with more unhealthy dietary behaviour
(Fleary et al., 2018; Gracey et al., 1996).
Food literacy attributes occur and impact dietary behaviour differently between males
and females. Most studies found that female adolescents had higher nutritional knowledge
(Bailey et al., 2019; Ghasab et al., 2017; Gracey et al., 1996; Sichert-Hellert et al., 2011;
Vaitkeviciute et al., 2014) and FVC (Bailey et al., 2019; Ghasab et al., 2013; Nagy-Pénzes et al.,
2020; Park et al., 2013; Ronto et al., 2016; Sahingoz & Sanlier, 2011; Souza Santos et al., 2019)
as compared to their male counterparts. However, these traits were not consistently attributed to
healthier diet behaviour, as more female adolescents skipped meals (Garrido-Ferándex et al.,
2020; Ghasab et al., 2013; Nagy-Pénzes et al., 2020; Park et al., 2013; Pearson et al., 2009;
Vaitkeviciute et al., 2014; Videon & Manning, 2003) and were either just as likely or more likely
to practice poor dietary behaviours as compared to males (Lai Yeung 2010; Park et al., 2013;
Sanchez et al., 2007; Sichert-Hellert et al., 2011). Ecological factors also influence females
differently than males, such as household education, which was found to impact the nutritional
knowledge scores of female adolescents more than males. However, when household education
was evaluated by sex, maternal education had the greatest impact on the dietary behaviours of
both sexes of adolescents (Bau et al., 2011; Pearson et al., 2009; Sachingoz & Sanlier, 2011).
Food and nutritional knowledge increased by approximately 2% a year with respect to the
age of survey respondents (Sichert-Hellert et al., 2011), yet dietary behaviours were poorer
(Pearson et al., 2009; Videon & Manning, 2003), possibly attributed to changing ecological
influences, with the home environment reported to have a lower influence in older adolescents

50

and social acceptability having a higher influence (Tarabashkina et al., 2016). Due to lack of
longitudinal studies and a lack of attention to development age within primary studies, the
impact age has on food literacy and dietary behaviours is difficult to assess.
Strengths and Limitations of Review
This review was based on a systematic methodological framework, study question and
key terms guided by the PIE model, it followed the PRISMA reporting guidelines, and the search
terms and analysis were guided by the food literacy framework developed by Thomas et al.
(2019).
A limitation of this review is that it only includes peer reviewed literature; a search was
not conducted for grey literature and as an emerging concept this methodology did not prioritize
inclusion of government papers describing population health initiatives that aim to improve
population food literacy. A second limitation, which is also reported in food literacy knowledge
synthesis studies (Bailey et al., 2019; Brooks & Begley, 2014), is that definitions, and
conceptualizations of the attributes of food literacy differ between studies, making it difficult to
establish concrete search terms, resulting in a broad range of studies and difficulty in establishing
definitive conclusions. A third limitation relates to potential response bias within the studies that
measure food literacy attributes based on surveys, questionnaires, or 24-hour recall, reported by
the respondent (n=22/28). Although the majority of these studies employed strategies to reduce
bias such as anonymous data collection and randomization strategies, the potential remains for
bias based on socially desirable responses and limit the introduction of new concept development
encouraged through open ended questions. Studies, such as Raber et al. (2018) that reviewed
observational data related to dietary behaviour show potential to limit recall and social

51

desirability bias, as well as mixed methodology studies such as Ronto et al (2016) where focus
groups allow for the exploration of emerging themes.
Conclusion
This literature review described the current state of the knowledge related to the
relationships between adolescent food literacy and dietary behaviours. The review identified that
food skills remain an underexplored attribute of food literacy and those interventions aiming to
change dietary behaviours should consider the incorporation of food skills within their programs.
However, SES and the availability of healthy food options within the home significantly impact
the ability of adolescents to develop food literacy and healthy dietary behaviours.
During study selection, there were limited studies that evaluated food literacy from the
perspective of the adolescent, resulting in merely 28 of 2,235 non duplicate citations being
included in the present review, of which only 5 touched on each food literacy heading within the
food literacy framework (Table 3). The 2013 Canadian community health survey (CCHS) rapid
response on food skills (Part 2) – mechanical skills and food conceptualization (FS2) is a
comprehensive data set that includes questions related to food skills that incorporate nutritional
knowledge and self-efficacy, as well as data associated with ecological attributes. This data set
offers an opportunity to further examine the impact food skills have on adolescent dietary
behaviours. Fruit and vegetable consumption represent a rational outcome variable to measure
healthy dietary food choices among adolescents, as it was the most commonly measured dietary
behaviour outcome among the primary studies (16/22 primary studies) included in this review
(Bau et al., 2011; Chartatos et al., 2018; Hanson et al., 2004; Lai Yeung, 2010; Nagy-Pénzes et
al., 2020; Orehek & Ferrer, 2018; Park et al., 2013; Pearson at al., 2009; Pitel et al., 2013; Raber
et al., 2018; Ronto et al., 2016; Sanchez et al., 2007; Souza Santos et al., 2019; Utter et al., 2013;

52

Videon & Manning, 2003). Thus, the purpose of the proposed research project is to conduct a
secondary analysis of the CCHS rapid response on FS2 to answer the research question, how
does food literacy affect FVC among Canadian adolescents?

53

Chapter 3: Methods
Data for the present research was obtained from a secondary analysis of the 2013
Canadian Community Health Survey (CCHS) Rapid Response on Food Skills (Part 2) –
mechanical skills and food conceptualization (FS2). A descriptive research design was employed
to describe the relationships between food literacy attributes of Canadian adolescents and FVC.
The data source, study design, sample population, variable selection, and employed statistical
analysis will be discussed in the following chapter.
Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food Conceptualization
Data Set
The Rapid Response on Food Skills (Part 2) – mechanical skills and food
conceptualization is a continuation of the rapid response on food skills part 1 - knowledge,
planning and transference of skills (Statistics Canada, 2013b). Each rapid response module is
independent of the other and can be asked separately (Statistics Canada, 2013b). Rapid response
surveys aim to obtain national estimates on an emerging or specific topic related to population
health (Statistics Canada, 2013a). The CCHS rapid response survey takes a maximum of two
minutes per interviewee and questions are asked of all CCHS respondents during a single
collection period of two months (Statistics Canada, 2013a). The survey was developed in
collaboration with specialists from Statistics Canada, federal and provincial departments and/ or
academic fields for computer assisted interviewing (CAI) (Statistics Canada, 2013a). The survey
was field tested prior to CCHS data collection for observation of respondent reaction to the
survey, time estimates for various sections, study response rates, and to test feedback questions
(Statistics Canada, 2013a).
The CCHS is an annual cross-sectional survey that targets a sample size of 65,000
respondents through a multi-stage sample allocation strategy (Statistics Canada, 2013a). To

54

control for potential volunteer bias, the CCHS employs controlled randomization sampling
strategies. Sample populations over the age of 18 years are selected using selection probabilities
within an area frame method that gives relatively equal importance proportionally to the
populations residing in 110 health regions (HR) and 10 provinces, excluding populations living
in the Territories, on Aboriginal reserves or settlements, within foster care, institutions, and fulltime members of the Canadian Forces. (Statistics Canada, 2013a). Aboriginal is the term used to
refer to people who “are usually those with ancestors who resided in North America prior to
European contact and who identify with one of the three Aboriginal groups listed on the
questionnaire: First Nations (North American Indian), Métis and Inuk.” (Statistics Canada,
2013c, p.73). Sample populations between the age of 12 and 17 years are selected by using the
Canadian Child Tax Benefit Frame, one child per household is selected to complete the survey
(Statistics Canada, 2013a). To minimize sampling errors, CCHS uses highly skilled and trained
interviewers (Statistics Canada, 2013a). Missing data related to partial completion was reported
as minimal, with the majority of missing data or non-response occurrences classified as a “total
non-response”, described as a selected respondent who refused to be interviewed or by the
inability of the interviewer to contact the selected respondent (Statistics Canada, 2013a). Missing
data attributed to “total non-response” were handled by adjusting the weight of persons who
responded to the survey based on the characteristics available for both the respondents and nonrespondents (Statistics Canada, 2013a).
Once a household had been selected, all members of the household are listed and all
persons over the age of 12 are recorded. Through various probabilities based on age and
household composition, one person from the household is selected to participate in the survey
(Statistics Canada, 2013a). Approximately 40% of the CCHS interviews are conducted using

55

computer assisted personal interviewing (CAPI) and 60% are over the phone using computer
assisted telephone interviewing (CATI) (Statistics Canada, 2013a). Strengths of computer
assisted interviewing (CAI) include a case management and data transmission functionality, as
well as the ability to customize interviews to the respondent’s characteristics and survey
responses, providing immediate feedback and edits to check for inconsistent answers (Statistics
Canada, 2013a). Interviews are conducted by an interviewer who is competent in the language
primarily spoken by the respondent (Statistics Canada, 2013a).
Responding to the survey is voluntary and data is collected directly from the respondents
(Statistics Canada, 2013a). In 2013, 61,103 individuals completed the CCHS survey, a response
rate of 67.0% (Statistics Canada, 2013a). In January and February of 2013, 15,723 respondents
were determined to be within the scope for the Food Skills (Part 2) rapid response, of which
12,072 households agreed to participate, with 10,701 individuals completing the survey,
representing a combined response rate of 68.1% (Statistics Canada, 2013b).
Rapid response sample weights are “calibrated by province by age group by sex, instead
of the usual Health region by age group by sex” (Statistics Canada, 2013b, p.2). Due to the
smaller sample sizes of the rapid response survey, it can be difficult to obtain high quality
estimates at a detailed level, hence the recommendation by Statistics Canada to create national
level estimates, or depending on sample size, provincial level estimates (Statistics Canada,
2013b). In consideration of the above recommendation, analysis was only conducted on
bootstrapped 1 results to ensure the quality of the sampling distribution was representative of the

Bootstrapping methods are procedures “that provide alternative ways to estimate the variance and standard error of
a parameter by repeated resampling from a sample. Bootstrapping is a nonparametric approach to statistical
inference that can be applied to any data because it required no assumptions about underlying population
distributions. ... “This resampling provides an estimate of what we would have gotten had we sampled repeatedly
from the population” (Vogt & Johnson, 2011, p.36).
1

56

overall population. Bootstrapping was employed to calculate percentages, standard error,
confidence intervals and coefficients of variation, and to test statistical significance of
differences.
Cross-sectional studies, such as the CCHS, collect large volume of data, and are useful
for calculating prevalence rates, recognizing trends and patterns, and serving as preliminary data
to support subsequent, more extensive research (Thompson & Panacek, 2006). The analysis of
national cross-sectional data collected in the CCHS FS2 was utilized to describe the prevalence
of food literacy characteristics within the Canadian adolescent population and the patterns or
relationships between the identified variables.
A request to access the confidential Master Data File for the 2013 CCHS Rapid Response
on Food Skills (part 2) – Mechanical skills and food conceptualization was made to Statistics
Canada for access to the regional data center (RDC) program; approval was received October 15,
2019. All analyses were conducted in and vetted through the RDC, as the CCHS FS2 is not
available as a Public Use Microdata Files (PUMF).
Sample Population
The study sample was drawn from households where adolescents aged 12 to 17 years of
age were selected as survey respondents. Only survey respondents who answered the questions
related to the dependent variable, total daily fruit and vegetable consumption (FVCGTOT), were
included. Responses to questions pertaining to highest level of household education attainment,
total household income, and geographical location (Province of residence) were answered by the
person most knowledgeable (PMK) in the household, and not the adolescent respondent selected
for the survey.

57

Variable Selection
The food literacy framework by Thomas et al. (2019) was used to guide the variable
selection and analysis. Figure 4 displays the food literacy framework and the selected survey
variables that aligned with each food literacy category. A complete list of study variables is
available in Appendix D. The CCHS FS2 was chosen for secondary analysis as it contains
questions pertaining to food skills that are reflective of food literacy attributes identified by
Thomas et al (2019). For example, questions that assess the individual characteristics of
nutritional knowledge, self-efficacy and food skills are reflected in questions related to the
respondent’s ability to use a knife or peel vegetables, to cook from basic whole ingredients
versus ready to eat and take-out options, to follow a recipe, or to adjust a recipe to make it
healthier. Dietary behaviour was assessed through questions pertaining to FVC and perceived
eating habits. The societal category (the meso-and macrosystem that interact with food
decisions) included variables assessing household socio-economic attributes, or SDH, including
highest level of household education attainment, total household income, and household
composition. Age, sex, immigration status, and survey respondents who identify as Aboriginal,
represent both population characteristics and SDH (CPHA, 2021; Office of Disease Prevention
and Health Promotion 2021) which are captured under the societal/ external factors. However,
the Aboriginal population identified within this survey are only representative of the survey
respondent’s who self identify as Aboriginal, and do not represent the larger Canadian
Aboriginal population.
Provincial residency was included to allowed for the comparison of variances and
similarities between provinces. Additional variables possibly associated with food literacy were
body mass index (BMI) and perceived health status (Vaitkevičiūtė & Petrauskienė, 2019.

58

Specifically, BMI has been negatively associated with nutrition and healthy dietary behaviours in
some studies (Vaitkevičiūtė & Petrauskienė, 2019), while perceived health status may influence
food skills activities and dietary behaviours.

59

Figure 4
Study Variables From Canadian Community Health Survey - Rapid Response on Food Skills
(Part 2) – Mechanical Skills and Food Conceptualization Data Set That Align With Categories
From the Food Literacy Framework by Thomas et al., 2019.

Note. Food literacy table adapted from “Complexities in conceptualizing and measuring food
literacy” by H. Thomas et al., 2019, Journal of the Academy of Nutrition and Dietetics, 119(4),
563-573. Variables represented in table are from the “Canadian Community the Health Survey,
Rapid Response on Food Skills (Part 2) – mechanical skills and food conceptualization, data set”
(Statistics Canada, 2013).
Statistical Analysis
Statistical analysis was conducted in SPSS (version 24) (IBM Corp., 2013) and STATA
(version 15) (Stata Corp, 2015) software. SPSS was used to apply the inclusion criteria for age
and to prepare the data for analysis. The data file was then merged with the bootstrapping and
master weights files and exported into STATA to allow data analysis to be conducted with

60

bootstrapped weights applied. Bootstrapping was employed to estimate distribution, to calculate
confidence intervals and coefficients of variation, and to test the statistical significance of
differences for all data analyses. The employment of bootstrapping procedures is intended to
allow for probability-based estimation of the population distribution from the survey data, by
applying an empirical technique “to estimate the variance and standard error of a parameter by
repeated resampling of a sample” (Vogt & Johnson, 2011, p.36). A significance level of p<0.05
was applied to all cases. The data analyses involved both descriptive and inferential statistical
tests. The relationships between independent and dependent variables were analysed through
correlational and regression statistical tests. The study used a non-experimental, descriptive
correlational research design. A descriptive research design was chosen as the study does not
involve the employment of an intervention, rather the intent was to describe relationships
between variables previously collected. This research design aligns with the study’s aim to
describe the relationship between food literacy attributes of Canadian adolescents aged 12 to 17
years and FVC using cross-sectional data collected in 2013.
Descriptive Analysis
Univariate statistical analysis described the study population and selected variables
numerically as percentages, numbers, and mean values. Variability within the data was described
by standard deviation.
Bivariate descriptive analysis between independent and dependent data were analyzed
using cross-tabulations, chi square, and simple logistic regression. The dependent variable was a
binary variable measuring the frequency of FVC daily, less than 5 times (FVC=0) and 5 times or
more (FVC=1), created from the variable FVCGTOT. The selection of FVC as an outcome
variable to measure healthy dietary behaviours is supported by the WHO (2020) obesity

61

prevention recommendations, which amongst several strategies, calls for increased adolescent
FVC. Consuming fruit or vegetables five or more times a day was selected as a measure of
healthy food behaviours for three reasons. First, a minimum intake of five servings of fruits and
vegetables per day is widely referenced in the literature as the recommended minimum intake if
fruits and vegetables internationally (Roark & Niederhauser, 2012), and approaches the
recommendations of Health Canada for daily servings of fruit and vegetable intake the year the
CCHS survey was conducted; seven to ten for adults, six for children aged 9-13, and seven to
eight for children aged 14-18 (Health Canada, 2019). Secondly, FVC was the most commonly
measured dietary behaviour outcome found within this studies literature review (Bau et al., 2011;
Chartatos et al., 2018; Hanson et al., 2004; Lai Yeung, 2010; Nagy-Pénzes et al., 2020; Orehek
& Ferrer, 2018; Park et al., 2013; Pearson at al., 2009; Pitel et al., 2013; Raber et al., 2018;
Ronto et al., 2016; Sanchez et al., 2007; Souza Santos et al., 2019; Utter et al., 2013; Videon &
Manning, 2003). Thirdly, the data collected in the survey used for this study has recorded fruit
and vegetable intake as less than five times a day, five to ten times a day, and more than 10 times
a day.
Results were reported as percentages and p values, which were derived from chi square
analysis, and odds ratios from simple logistic regression analysis. Assumptions for the chi square
tests and simple logistic regression models of n quota were evaluated within the RDC based on
the non-bootstrapped sample. When variables failed to meet the assumption of n, they were
recoded until the assumption for n was met. The most appropriate variable categories were
recoded based on clinical applicability of the existing categories to meet the n quota. Variable
categories titled ‘don’t know’, ‘refused’, or ‘not stated’, were recoded as ‘missing’ data, and

62

while included in the description of the population, missing data were not included in the chi
square or logistic regression models.
Logistic regression models were examined for multicollinearity among included
variables. Variables that were significant at the bivariate level were considered for inclusion at
the multivariate model level, and in some cases non-significant variables were also included as
possible predictors if they were significant within the literature or based on clinical experience.
Covariates identified within the literature were selected based on significant relationships to food
literacy and adolescent FVC, including sex and highest level of household education attainment.
Although total household income had statistically significant relationships to adolescent FVC at
the bivariate level, it was not selected as a covariate at the multivariate level due to the lack of
consistency as a predictor of SES within the literature. Within the literature, income was most
frequently used as a measure of SES in combination with household education, rather than as an
independent variable (Bau et al., 2011; Hanson et al., 2004; Nagy-Pénzes et al., 2020; SichertHellert et al., 2011; Souza Santos et al., 2019). Additionally, income as a variable may not be
representative of SES due to cost of living variations geographically across Canada, which
cannot be controlled for with the level of geographical data available within the CCHS FS2.
Despite the sample being bootstrapped, the actual samples (n) in less populated provinces are
low, thus data pertaining to provincial populations should be used with caution and may not be
generalizable to the populations of these regions. Additionally, provincial data is not broken
down by urban verses rural, or by the socioeconomics of neighbourhoods or regions, as such it is
difficult to ascertain the ecological or environmental impact that provincial geography has on
FVC of Canadian adolescents. To account for the aforementioned concerns related to provincial
samples, the variable geo_prov was removed from the final model. The variable adding

63

vegetables to a recipe to make it healthier was also removed from the multi logistic regression
models due to multicollinearity concerns associated with the variable adjusting a recipe to make
it healthier’, which was retained in the final model.
Multivariate Analysis
Logistic regression was selected to analyse the relationship between food literacy
variables and FVC, as this type of analysis allows for the use of both categorical and continuous
independent variables within the analysis, thereby expanding the number of independent
variables to be analysed. The final logistic model was developed from variables that had a
statistically significant relationship to FVC at the bivariate level. Variables not showing
statistical significance at the bivariate level were considered for inclusion if identified within the
literature or clinical practice as important, such as sex.
The goodness of fit test, estat gof postestimation command for the Hosmer-Lemeshow
goodness-of-fit test statistic, was reported as an indicator of explanatory power for the final
logistic regression model (Xing, 2016). Although the use of data sets containing large samples,
such as the Statistics Canada survey data, “is thought to mitigate concerns about potential
influential observations by minimizing the contribution of any given observation”, influential
outliers can still arise in these large samples (Ryan et al., 2015, p.4). The required diagnostic
statistics through STATA to identify the confidence interval displacement diagnostic (C
diagnostic) and the DFbeta diagnostic (the standardized difference in the parameter estimate due
to deleting given observations) are not available for bootstrapped data, therefore it has been
recommended to assess the final model for outliers, leverage, and influence (Ryan et al., 2015;
Zhang, 2016). Influential observation analysis was conducted on the model. Multivariate outliers
were identified, and the model was run again excluding these individuals. The results were

64

assessed for changes in the sign or direction of the odds ratios or changes in significance. No
significant shift of the coefficient greater than 14%, nor change in significance of included
variables were observed, therefore all cases were left in the final model.

65

Chapter 4 – Results
An overview of the study sample, including food literacy individual characteristics and
societal/ external food literacy factors are provided in Chapter 4. The differences between the
likelihood of study variables predicting FVC in Canadian adolescents are also discussed. Finally,
the results of the logistic regression model will be discussed. Study results specify whether the
reported N values are representative of the non-bootstrapped or the weighted bootstrapped
sample. The reported statistical measures, including percentages, p values, and odds ratios are
representative of the weighted bootstrapped data analyses run in STATA.
Study Sample Characteristics
Data from 790 Canadian adolescent (age 14.7 ±0.14 years; range 12-17 years) were
included in the current analysis. This population was drawn from 896 households where
adolescents aged 12 to 17 years of age were selected and responded to the 2013 CCHS FS2.
Sample characteristics are shown in Table 4. All data are reflective of answers provided directly
by the adolescent selected for the survey, unless indicated that the question was answered by the
person most knowledgeable (PMK) within a household.

66

Table 4
Descriptive Characteristics of Study Sample, Canadians, 12 to 17 Years of Age, Canadian
Community Health Survey – Rapid Response on Food Skills (Part 2) – Mechanical Skills and
Food Conceptualization 2013 (N=790), (Weighted N=2,127,482).
Descriptive sample variables

Total population
weighted sample
100%

Weighted
N=2,127,482

Non weighted
sample N=790

50.2
49.8

1,067,145
1,060,337

409
381

13.8
13.2
18.6
19.7
18.4
16.4

293,380
279,977
395,073
419,965
390,818
348,269

112
124
138
142
137
137

12.5

265,510

68

86.5
1.0

1,841,123
20,888

714
8

5.1

107,800

66

81.1
12.5
1.4

1,725,175
265,084
29,529

640
68
16

10.5
20.4
68.5
0.5

224,237
434,645
1,458,176
10,697

84
136
565
5

68.2
31.8
0.0

1,451,368
676,114
0

554
236
0

Sex
Male
Female
Age
12
13
14
15
16
17
Immigration status of adolescent
Country of birth outside of
Canada
Born in Canada
Missing
Aboriginal identity (Self identified)
Aboriginal (First nations,
Metis, Inuit)
Not Aboriginal
Not applicable a
Missing
Living arrangements of adolescent
respondent
Not living with parent
Living with single parent
Living with 2 parents
Missing
Self-perceived health
Very good to excellent
Poor, fair, or good
Missing
Respondents’ opinion of own
weight (self reported)

67

Just about Right
Under or over weight
Missing
Body mass index (self-reported)
Neither overweight nor obese
Overweight/ obese
Missing
Fruit and vegetable consumption
Less than 5 time a day
5 or more times a day
Eating habits
Very good to excellent
Poor, fair, or good
Missing
Province of residence of respondent
(PMK)
Newfoundland and Labrador
Prince Edward Island
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia

73.5
21.3
5.2

1,564,338
453,579
109,736

592
153
45

71.6
19.3
9.1

1,522,852
410,179
194,409

539
170
81

56.4
43.6

1,200,751
926,731

436
354

45.7
52.5
1.8

972,898
1,116,928
37,742

381
396
13

1.7
0.5
2.9
2.1
21.3
40.5
3.3
2.8
11.4
13.5

35,252
11,284
62,399
43,933
453,366
861,843
70,441
59,910
242,107
286,997

28
15
31
29
128
318
39
36
78
88

Note: Percentage values are representative of bootstrapped data. PMK= person most
knowledgeable in household. a Adolescent respondent born outside Canada, US, Germany, and
Greenland.

68

The study sample represents a bootstrapped sample of 2,127,482 of the Canadian national
population aged 12 through 17. Statistics Canada weights data at the person-level to ensure
weights sum up to known population totals, calibrated by province, age group, and by sex
(Statistics Canada, 2013b). The weighted sample was comprised of 50.2% male and 49.8%
female, and more than half of the respondents lived in homes with two parents (68.5%), while
20.4% lived in single parent households, and 10.5% reported not living with a parent. A higher
percentage of study respondents came from households reporting higher annual incomes, 42.2%
from households with income greater than $90,000, compared to 13.6% from households with
incomes less than $29,999 (Figure 5). A higher percent of respondents came from households
with higher education; 37.2% were from households where the highest level of household
educational attainment was a bachelor’s degree or higher, compared to 16.8% from households
where the highest level of household educational attainment was high school graduation (Figure
5).
Figure 5
Distribution of Total Household Income From all Sources, Canadians, 12 to 17 Years of Age,
Canadian Community Health Survey – Rapid Response on Food Skills (Part 2) – Mechanical
Skills and Food Conceptualization 2013 (N=790).
13.6%

Total Household
income
<$29,999

42.2%
22.7%

$30,000-$59,999
$60,000-$89,999

21.6%

Note. All dollars are in Canadian currency.

>$90,000

69

Figure 6
Highest Level of Household Educational Attainment, Canadians, 12 to 17 Years of Age,
Canadian Community Health Survey – Rapid Response on Food Skills (Part 2) – Mechanical
Skills and Food Conceptualization 2013 (N=790).
< Secondary school graduation

3.9%

Secondary school graduation

16.8%

Some post-secondary education

2.8%

Trades certificate or diploma

7.6%

Legend

Diploma or certificate <…
≥Bachelor's degree
missing

Allocation within sample
population

26.1%
37.2%
5.6%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

Approximately two thirds of respondents reported their health as very good or excellent
(68.2%), compared to those who reported their health as poor, fair, or good (31.8%). Similarly,
approximately three quarters of respondents reported their opinion of their weight as just about
right (73.5%), compared to those who reported their weight as under or over (21.3%). Even
though the demographic description in Table 4 shows the respondent’s opinion of their weight as
just about right closely aligned with the percentage of respondents who were neither overweight
nor obese (71.6%) as measured by the BMI, these two variables were not run for comparison
purposes for each respondent to confirm alignment. Over half of respondents (56.4%) consumed
fruits and vegetables less than five times a day. However, the mean FVC among the total sample
population approaches consumption of fruits and vegetables five or more times, at 4.9 (±0.3)
servings a day. Just under half of respondent’s reported their eating habits as very good to
excellent (45.7 %).
Study variables representing individual food literacy characteristics are displayed in
Figure 7. As evident in figure 7, when food skills were more complex, a lower percent of

70

respondents reported high confidence in the skill. For example, while 85.6% of respondents
reported high confidence in the safe use of a knife, only 48.6% reported high confidence in their
ability to freeze raw vegetables, and 26.1% in their ability to can foods in glass jars. This pattern
is also evident in a respondent’s ability to prepare food, were as food preparation variables
become increasingly complex and require a higher level of food and nutritional knowledge,
fewer respondents report high levels of skill. For example, while nearly three quarters of
respondents reported high confidence in their ability to bake from a mix (73.2%), only 59.5% of
respondents reported high confidence in their ability to bake from scratch, and about a third
(33.6%) reported high confidence in their ability to adjust a recipe to make it heathier.
Figure 7

Frequency of respondents

Sample Variables Descriptive of Individual Food Literacy Characteristics, Canadians, 12 to 17
Years of Age, Canadian Community Health Survey – Rapid Response on Food Skills (Part 2) –
Mechanical Skills and Food Conceptualization 2013 (N=790).
100%
75%
50%
25%
0%

Food Skills - Individual food literacy characteristics
High skill

Low skill

missing

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Differences Between Food Literacy Characteristics and Their Relationship to FVC
The relationships between the selected food literacy variables and the probability of
consuming fruits and vegetables five or more times per day are displayed in Table 5.
Independent variables were grouped under food literacy categories that best aligned with each
variable; however, food literacy characteristics are interconnected and as such there are overlaps
between categories where variables fit. Although the CCHS FS2 records total daily FVC as
servings per day, it should be noted when interpreting results that this is a measurement of FVC
as “the number of times (frequency), not the amount consumed” (Statistics Canada, 2013a, p.99).

Male
Female

12
13
14
15
16
17
Self-Perceived health
Very good to excellent
Poor, fair, or good
Body mass index (self-reported)
Neither overweight nor obese
Overweight/ obese

Age

Sex

58
58
46
56
59
63
53
64
54
61

42
42
54
44
41
37
47
36
46
39

53
59

Weighted 100 %
N=2,127,482

Weighted 100 %
N=2,127,482
47
41

Fruit and
vegetable
consumption of
less than 5
servings a day
(FVC=0)

Fruit and
vegetable
consumption of 5
or more servings
a day (FVC=1)

0.2

0.03

0.4

0.2

P
value

1.33

1.57

1.22
1.22
2.03
1.32
1.18

0.78

[0.84, 2.11]
Ref

[1.05, 2.34]
Ref

[0.61, 2.41]
[0.60, 2.52]
[1.04, 3.95]
[0.68, 2.57]
[0.58, 2.39]
Ref

Ref
[0.52, 1.16]

Unadjusted odds
ratio 95% CI

Sample Population Characteristics by Reported Fruit and Vegetable Consumption for Canadians, 12 to 17 Years of Age, Canadian
Community Health Survey – Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food Conceptualization 2013 (N=790).

Table 5

72

Respondents’ opinion of own weight (self reported)
Just about right
Under or over weight
Individual food literacy characteristics
Skills-cooking from basic Ingredients
High
Low
Skills - Knife
High
Low
Skills - Peeling, chopping, slicing
High
Low
Skills - Freezing
High
Low
Skills – Canning
High
Low
Skills - Meat
High
Low
Skills -Stew, soup, casserole
High
Low
Skills - Baking from mix
High
Low
Skills-Bake from scratch, recipe
52
69

53
67
57
53
55
62
51
61
53
57
53
60
50
60
55
60

48
31

47
33
43
47
45
38
49
39
47
43
47
40
50
40
45
40

0.1

0.4

0.1

0.2

0.5

0.05

0.2

0.6

0.01

0.006

1.23

1.45

1.31

1.16

1.50

1.35

0.85

1.84

2.03

[0.73, 2.07]
Ref

[0.98, 2.15]
Ref

[0.88, 1.94]
Ref

[0.76, 1.78]
Ref

[1.00, 2.24]
Ref

[0.84, 2.16]
Ref

[0.46, 1.56]
Ref

[1.12, 3.02]
Ref

[1.23, 3.37]
Ref

73

Yes
No
Dietary behaviour
Eating habits
Very good to excellent
Poor, fair, or good
Societal characteristics
Immigration Status of adolescent
Country of birth reported outside of Canada
Born in Canada
Aboriginal identity (Self identified)
Aboriginal (First nations, Metis, Inuit)
Not Aboriginal
Not applicable a
Living arrangements of adolescent respondent
Not living with parent
Living with single parent
Living with 2 parents
Income - Total household income from all sources (PMK)
≤$29,999
$30,000-$59,999
$60,000-$89,999

Added more vegetables or fruit

High
Low
Adjust a recipe, healthier
Yes
No
How did you make it healthier?
46
44

41
70

69
55
52
55
69
42
38
46
48
67
61

59
30

31
45
48
45
31
58
62
54
52
33
39

45
62

55
38

54
56

53
61

47
39

0.02

0.4

0.1

0.1

<0.001

0.8

0.002

1.11
0.50
0.64

0.86
1.19

1.85
0.49

1.85

3.34

0.92

2.05

1.36

[0.58, 2.13]
[0.30, 0.82]
[0.39, 1.07]

Ref
[0.38, 1.94]
[0.62, 2.29]

Ref
[0.42, 1.89]
[0.19, 1.31]

Ref
[0.91, 3.78]

[2.25, 4.95]
Ref

[0.45, 1.88]
Ref

[1.29, 3.26]
Ref

[0.91, 2.05]
Ref

74

≥$90,000
Highest level of education – household, 6 types (PMK)
Did not complete secondary school
Completed secondary school
Some post-secondary education
Trade’s certificate or diploma
Diploma or certificate below a bachelor’s degree
Bachelor's degree or higher
Province of residence of respondent (PMK)
Newfoundland and Labrador
Prince Edward Island
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
Preparation of main meal
Mostly whole foods
Not mostly whole foods
Do you grow vegetables, herbs, or fruit at home or in a
community garden
Yes
No
51
35
64
34
77
56
52
70
50
60
46
40
57
84
77
62
64
55
59

55
58

49
65
36
66
23
44
48
30
50
40
54
60
43
16
23
38
36
45
41

45
42

0.5

0.6

0.001

0.02

1.14

1.15

0.79
1.85
1.21
2.13
2.67
1.38
0.35
0.53
1.12

2.05
0.61
2.18
0.34
0.88

[0.77, 1.69]
Ref

[0.72, 1.86]
Ref

[0.25, 2.48]
[0.40, 8.55]
[0.42, 3.50]
[0.71, 6.34]
[1.32, 5.43]
[0.75, 2.57]
[0.13, 0.95]
[0.15, 1.84]
[0.55, 2.29]
Ref

[0.68, 6.11]
[0.31, 1.20]
[0.50, 9.57]
[0.14, 0.81]
[0.55, 1.41]
Ref

Ref

75

a

Adolescent respondent born outside Canada, US, Germany, and Greenland.

confidence intervals (CIs) are based on weighted data. PMK= person most knowledgeable in household.

Note. Chi-square analyses (p values) are based on weighted data. Simple logistic regression analyses, odds ratios (ORs) and 95%

76

77

Respondents whose opinion of their weight was just about right were more likely to
consume five or more servings of fruits and vegetables a day compared to those who viewed
their weight as under or over (OR 2.03, 95% CI 1.23-3.37). However, both groups of
respondents in relation to their opinion of their weight were more likely to consume fruits and
vegetables less than five times a day than to consume five or more times a day. Only 48%
percent of respondents who reported opinion of their weight as just about right and 31% of those
who reported opinion of their weight as under or over consumed fruits and vegetables five or
more times a day. Respondents who reported their perceived health as very good to excellent
had a greater likelihood of consuming fruits and vegetables five or more times a day compared to
those who reported their health as poor, fair, or good (OR 1.57, 95% CI 1.05-2.34).
Individual Food Literacy Characteristics
When compared to respondents who report low levels of food skill, respondents were
more likely to consume fruits and vegetables five or more times a day when they reported high
skill levels in the ability to cook from basic ingredients (OR 1.84, 95% CI 1.12-3.02), freeze
vegetables from raw (OR 1.50, 95% CI 1.00-2.24), and to adjust a recipe to make it healthier
(OR 2.05, 95% CI 1.29-3.26). As previously identified, despite respondents who report higher
skill levels having a higher probability of consuming fruits and vegetables five or more times a
day compared to those who report low skill levels, those who report a high skill level continue to
have a higher frequency of consuming fruits and vegetables less than five times a day compared
to five or more times. For example, the frequency of respondents consuming fruits and
vegetables less than five times a day was higher than 50% for those who reported high skill
levels in the ability to cook from basic ingredients (53%) and to freeze vegetables from raw
(51%). The ability to adjust a recipe to make it healthier was the only food skill variable where

78

respondents who reported a high skill level also reported higher frequency of consuming fruits
and vegetables five or more times a day (55%). However, although a respondent’s ability to
adjust a recipe to make it healthier had a significant relationship to FVC (p=0.002), the ability to
adjust a recipe by adding fruits and vegetables did not show a significant relationship with FVC
(p=0.8).
Dietary Behaviours
Dietary behaviours, in addition to being measured by the outcome variables FVC, were
also reflected in the variable where respondents were asked to rate their eating habits. Perceived
eating habits remained a significant variable throughout analyses, at both the bivariate (p<0.001)
and multivariate (p<0.001) levels of analyses. Respondents who reported very good to excellent
eating habits had a significantly higher likelihood of consuming fruits and vegetables five or
more times a day (59%), compared to those who reported poor, fair, or good eating habits (30%)
(OR 3.34, 95% CI 2.25-4.95).
Societal Food Literacy Characteristics and FVC
Socio-economic measures available within the dataset that had a significant relationship
to FVC included household income (p=0.02), highest level of household education attainment
(p=0.02), and province of residence (p=0.001). Higher levels of household income were not
associated with higher probabilities of consuming fruits and vegetables five or more times a day.
Respondents from households with income between $30,000-$59,999 were less likely to
consume fruits and vegetables five or more times a day compared to those who came from
households with income over $90,000 (OR 0.50, 95% CI 0.30-0.82). However, compared to
respondents who came from households with income over $90,000, the remaining two income
brackets of less than $29,999 (OR 1.11, 95% CI 0.58-2.13) and $60,000-$89,999 (OR 0.64, 95%

79

CI 0.39-1.07) were not found to have significantly higher or lower odds of consuming fruits and
vegetables five or more times a day.
Similar to income, higher levels of household educational attainment were not associated
with higher probabilities of consuming fruits and vegetables five or more times a day. Compared
to households where the highest level of educational attainment was a bachelor’s degree or
higher, households with lower educational attainment, apart from trades, did not show significant
odds of higher or lower likelihood of consuming fruits and vegetables five or more times a day
(Table 5). Households where trades education represented the highest level of household
education attainment, when compared to households where the highest level of educational
attainment was a bachelor’s degree or higher, had lower odds of consuming fruits and vegetables
five or more times a day (OR 0.34, 95% CI 0.14-0.81). Households where trades education
represented the highest household education attainment, also had the lowest frequency (23%) of
consuming fruits and vegetables five or more times a day compared to all other levels of
household education.
More respondents from Quebec (60%) and New Brunswick (54%) consumed fruits and
vegetables five or more times a day as compared to those who consumed fruits and vegetables
less than five times a day. Compared to respondents from the province of British Columbia,
respondents from Quebec (OR 2.67, 95% CI 1.32-5.43) had higher odds of consuming fruits and
vegetables five or more times a day, and respondents from Manitoba lower odds (OR 0.35, 95%
CI 0.13-0.95).
Predictive Model Results
The results from the final logistic regression model are displayed in Table 6. The final
regression model was bootstrapped and replicated 500 of the desired 500 replications, had a

80

significant p value of less than 0.001, and a goodness of fit of Prob>chi= 0.19, indicating a good
fit. Logistic regression analyses found sex to have a significant relationship with FVC (p=0.04),
with male respondents significantly more likely to consume fruits and vegetables five or more
times a day compared to females (OR 1.73. 95% CI 1.03-2.88). Additionally, respondents who
reported very good to excellent eating habits had a significantly higher probability of consuming
fruits and vegetables five or more times a day compared to those who report poor, fair, or good
eating habits (OR 2.91, 95% CI 1.84-4.62). Furthermore, those from households where the
highest level of household educational attainment was trades were less likely to consuming fruits
and vegetables five or more times a day than those from households where the highest level of
household educational attainment was a bachelors’ degree or higher, (OR 0.36, 95% CI 0.150.87). An analysis conducted to compare differences between the respondents that were included
(n=674) and excluded for missing data (n=116) for the final logistic regression model found no
significant difference between the group’s likelihood of consuming fruits and vegetables five or
more times a day (p=0.1).

81

Table 6
Logistic Regression Model Predicting Probability of Consuming Fruits and Vegetables Five or
More Times a Day for Canadians, 12 to 17 Years of Age, Canadian Community Health Survey –
Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food Conceptualization 2013
(n=674).
Variable
Sex (Ref = female)
Male
Opinion of weight (Ref = under or over)
Very good to excellent
Eating habits (Ref = poor, fair, or good)
Very good to excellent
Cooking from basic ingredients (Ref = low)
High
Freezing raw vegetables (Ref= low)
High
Adjust a recipe to make it healthier (Ref = low)
High
Highest level of household education
attainment (Ref = bachelor’s degree or
higher)
Below High School
High School
Some Post Secondary Education
Trades
Certificate or Diploma
Note: goodness of fit of Prob>chi= 0.19.

Adjusted odds
ratio (95% CI)

Pvalue

SD (z)

(SE)

2.08

-0.45

1.73

[1.03,2.88]

0.04

1.57

-0.44

1.55

[0.90,2.70]

0.1

4.54

-0.69

2.91 [1.84, 4.62]

<0.001

0.71

-0.42

1.27 [0.66, 2.44]

0.5

1.53

-0.37

1.47 [0.90, 2.40]

0.1

1.78

-0.52

1.71 [0.95, 3.10]

0.1

1.35
-1.14
0.72
-2.27
-0.32

-1.61
-0.25
-1.38
-0.16
-0.26

2.44 [0.67, 8.87]
0.64 [0.30,1.38]
1.76 [0.38, 8.16]
0.36 [0.15,0.87]
0.91 [0.53,1.59]

0.2
0.3
0.5
0.02
0.8

82

Chapter 5 - Discussion
Study results found a positive relationship between several attributes of food literacy and
FVC among Canadian adolescents. While basic or simple food skills alone were not found to
have a relationship, complex and multifaceted food skills were found to positively influence
adolescent FVC. The relationships found between multifaceted food skills and adolescent FVC
suggest that food literacy attributes are interconnected and have reciprocal relationships. Societal
factors and an adolescent’s perception of their eating habits had a significant relationship to their
likelihood of consuming fruits and vegetables five or more times a day and were found to have a
relationship with adolescent FVC at both the bivariate and multivariate levels of analyses.
Although high skill levels in multifaceted food skills were found to have positive relationships to
adolescent FVC at the bivariate level of analysis, when the highest level of household
educational attainment, sex, and perceived eating habits were controlled for, these food skill
variables were not found to have a significant relationship with adolescent FVC, suggesting that
societal factors potentially influence individual food literacy characteristics.
The food literacy framework developed by Thomas et al. (2019) used to guide the
following discussion, groups food literacy categories under three headings: individual
characteristics, dietary decisions, and societal/ external factors. Figure 8 displays the variables
that were found to have a significant relationship with adolescent FVC, and the corresponding
food literacy category. It should be noted, however, that the food literacy categories and
attributes operate in an interdependent manner (Azevedo Perry et al., 2017; Thomas et al., 2019),
and as such it is difficult to discuss each category in isolation.

83

Figure 8
Food Literacy Variables Found to be Predictive of Adolescent Consumption of Fruits and
Vegetables Five or More Times a Day, for the Sample Population, Canadians, 12 to 17 Years of
Age, Canadian Community Health Survey – Rapid Response on Food Skills (Part 2) –
Mechanical Skills and Food Conceptualization 2013 (N=790).

Note. Food literacy table adapted from “Complexities in conceptualizing and measuring food
literacy” by H. Thomas et al., 2019, Journal of the Academy of Nutrition and Dietetics, 119(4),
563-573.

= Variables found in the bivariate analyses to have a significant relationship with

adolescent fruit and vegetable consumption.

= Variables found to have relationships with

adolescent fruit and vegetable consumption at the multivariate level of analyses.
Individual Food Literacy Characteristics and FVC
Individual food literacy attributes that were found to have a relationship with Canadian
adolescent FVC were multifaceted, comprising of several food literacy attributes. These
multifaceted food skills were complex in nature, requiring the ability to perform several food
skills to complete, however the basic food skills that are the building blocks of these more

84

complex skills were not found to have a relationship with adolescent FVC. Adolescents who
reported high skill levels in three multifaceted food skills - the ability to cook from basic
ingredients, freeze vegetables from raw, and to adjust a recipe to make it healthier - were found
to have a higher probability of consuming fruits and vegetables five or more times a day
compared to those who reported low skill levels.
Food Skills
Adolescents who report high confidence in their ability to freeze vegetables from raw,
cook from basic ingredients, and adapt a recipe to make it healthier require the ability to perform
multiple basic mechanical food skills, such as chopping, measuring, cooking, and reading
recipes, and as such are multifaceted involving many factors to complete. The positive
relationships found in this study between multifaceted food skills and adolescent FVC is
consistent with previous studies that have concluded that food skills include more than simple
technical skills such as chopping (Azevedo Perry et al., 2017), and that programs which teach
multifaceted food skills, such as the ability to cook from basic ingredients, have been found to be
positively associated with FVC (Bailey et al., 2019; Brooks & Begley, 2014). Interestingly, at
the bivariate level of analyses, the ability to perform basic skills such as the safe use of a kitchen
knife, and peeling, chopping, and slicing, were not found to have a significant relationship with
adolescent FVC, suggesting that food skills that have relationships with FVC involve more than
the performance of a mechanical skill. This study’s findings add to the growing body of research
surrounding the relationship between food skills and dietary behaviours, suggesting that the level
of food skill development, and or the complexity of the food skill, may have differing impacts on
the relationship between a food skill and dietary behaviour.

85

Food and Nutritional Knowledge
Food skills that were multifaceted in nature were found to have a significant relationship
with adolescent FVC, further highlighting how these multifaceted food skills are reflective of
other food literacy attributes, such as food and nutritional knowledge. For example, to adjust a
recipe to make it healthier, an adolescent must have the food and nutritional knowledge to know
what constitutes a healthy meal, and that health choices have a positive influence on their present
and future well being. The positive relationship found between adolescent FVC and those who
report high skill levels in the ability to adjust a recipe to make it healthier is consistent with
relationships between food and nutritional knowledge and FVC found within the literature,
where increased food and nutritional knowledge was associated with higher FVC (Bailey et al.,
2019; Fleary et al., 2018; Lake et al., 2004; Souza Santos et al., 2019). Furthermore, food and
nutritional knowledge create the conditions for change (Vaitkeviciute et al., 2014), and as such
may contribute to an adolescent’s decision to learn how to adjust a recipe to make it healthier.
Self-Efficacy and Confident
The ability to adjust a recipe to make it healthier or cook from basic ingredients are
potentially reflective of higher levels self-efficacy and confidence. The desire to learn how to
prepare food and adjust a recipe to make it healthier is representative of the attribute of food
attitude, captured under the food literacy category of self-efficacy and confidence (Thomas et al.,
2019). The attributes of food and nutritional self-efficacy and cooking self-efficacy are also
reflected in the ability to adjust a recipe to make it healthier, as an adolescent who reports high
confidence in this food skill likely believes in their ability to apply food and nutritional
knowledge to prepare healthy meals. The positive relationships found between food skills that
potentially require higher levels of self-efficacy and adolescent FVC are consistent with other

86

studies which have found programs that incorporated food skills within their interventions were
positively associated with cooking self-efficacy (Bailey et al., 2019; Vaitkeviciute et al., 2014),
and higher self-efficacy scores were associated with healthier food choices (Gracey et al., 1996).
The reciprocal relationship between the food literacy attributes further emphasises the
multifaceted nature of the food skills that have a significant relationship with adolescent FVC.
Dietary Behaviours and FVC
The significant relationship between an adolescents’ perceived eating habits and FVC at
both the bivariate and multivariate analyses is suggestive of a high health value placed on FVC.
The current study found that adolescents who viewed their eating habits as very good or
excellent had a higher likelihood of consuming fruits and vegetables five or more times a day.
This finding could be supportive of the idea that a healthy attitude towards diet may result in
higher FVC and may be reflective of healthy diet choices. These findings support previous study
findings that have reported that adolescents perceive FVC as a component of a healthy diet
(Bailey et al., 2019; Sahingoz & Sanlier, 2011). A study by Ronto et al. (2016) found adolescents
to believe that having a healthy attitude or placing a high value towards healthy eating would
result in a healthy diet and lifestyle. Additionally, a study by Gracey et al. (1996) found
adolescents who had a healthy attitude towards diet, where they tried to eat healthy or considered
their food choices to be healthy, had a higher self-efficacy score, and that higher self-efficacy
scores were associated with healthier food choices. These studies are consistent with this study’s
findings and may support a link between value and attitude placed on diet and dietary behaviour.
Societal/ External Factors and FVC
Total household income, the highest level of household educational attainment,
geography, sex, and an adolescent’s opinion of their weight were all found within the current

87

study to have significant relationships to an adolescent’s likelihood of consuming fruits and
vegetables five or more times a day. However, the relationships found between the societal/
external factors and adolescent FVC within this study differed from those identified within the
literature.
Household Income
This study found that adolescent FVC was not associated with increasing total household
income. Fruit and vegetable consumption was found to be lowest among those from households
in the lower middle-income bracket of $30,000-$59,999. Adolescents who came from
households in the highest income bracket of greater than $90,000 were more likely to consume
fruits and vegetables five or more times per day, compared to those who came from households
that fell in the income bracket of $30,000- $59,999. However, the odds of adolescents from
households with total income below $29,999, or those between $60,000-$89,999, were not found
to be significantly different from those with total household incomes over $90,000. The results
associated with income are contradictory to those found within the literature review, where
increasing household income was often associated with healthier dietary behaviours, including
increased FVC (Fleary et al., 2018; Ghasab et al., 2017). The inconsistency found between
adolescent FVC, and total household income suggests that increasing household income is not
always associated with healthier dietary behaviours, such as higher FVC, nor is income the most
reliable measure of societal factors affecting dietary behaviours.
Household Education
Within the literature, the highest level of household educational attainment has
increasingly been used as a measure of SES instead of household income (Bau et al., 2011;
Ghasab et al., 2017; Pearson et al., 2009; Pitel et al., 2018; Sahingoz & Sanlier, 2011; Sichert-

88

Hellert et al., 2011), hence at the multivariate level of analyses this study used the variable
highest level of household educational attainment as a measurement of SES. Although the
highest level of household education attainment was found to have a significant relationship to
the likelihood of Canadian adolescents consuming fruits and vegetables five or more times a day,
the study results were mixed. The final logistic regression model found that adolescents from
households with higher levels of household education attainment (bachelors or higher) were
significantly more likely to consume fruits and vegetables five or more times a day, when
compared to households where the highest level of household educational attainment was trades.
Additionally, bivariate analyses found that adolescents from households where the highest level
of educational attainment was below high school, some post secondary education, less than a
bachelor’s degree, or bachelor’s degree or higher, had a higher frequency of consuming fruits
and vegetables five or more times a day, compared to those who came from household with high
school education or trades education. These results differ from those found in the literature,
where increasing levels of household educational attainment have been associated with increased
levels of FVC and healthier dietary behaviours (Bau et al., 2011; Chartatos et al., 2018; Fleary et
al., 2018; Garrido-Fernández et al., 2019; Ghasab et al., 2017; Nagy-Pénzes et al., 2020; Pearson
at al., 2009; Pitel et al., 2013; Sahingoz & Sanlier, 2011). The findings in this study suggest that
not all levels of post secondary education are associated with higher adolescent FVC.
Geography
Geography presents as an underexplored societal/ external factor that warrants further
investigation in relation to its potential impact on the relationship between household education
and FVC. Adolescents from the province of Quebec had higher odds of consuming fruits and
vegetables five or more times a day, and those from the province of Manitoba had lower odds

89

compared to adolescents from the province of British Columbia. The variations within provincial
social and health policy fall outside the scope of this study, however there are potential
relationships between geography, household education, and FVC that present an opportunity for
future study. For example, in 2011 “census metropolitan areas (CMA) had higher proportions of
people with university degrees (30.9%) and lower proportions of people with a trades certificate
(10.1%), compared with the national averages for university degrees (25.9%) and trades
certificates (12.1%)” (Ministry of Industry, 2013, p.17), suggesting the reverse in more rural
areas. Studies have suggested that access to nutritious food is poorer in rural areas, however few
studies regarding food access and environment have been conducted in Canada outside of the
CMA (Health Canada, 2013), therefore, regions outside of the CMA potentially have more
families who have the highest level of household educational attainment of trades, and/ or may
have different challenges in accessing healthy foods compared to those who reside in CMA, as
such these populations may require different health initiatives. It is not possible to make these
associations within the available data set analyzed, as the geographical data available does not
account for rural/ urban variations, nor identifies geography beyond provincial residency.
Sex
Contrary to findings within the literature where female adolescents were found to have a
greater likelihood of higher FVC compared to males (Bailey et al., 2019; Ghasab et al., 2013;
Park et al., 2013; Pearson et al. 2009; Sahingoz & Sanlier, 2011), this study found that when
controlling for highest level of household education, the ability to cook from basic ingredients,
freeze vegetables from raw, adjust a recipe to make it healthier, and adolescents’ perceived
eating habits, males were found to have a higher likelihood of consuming fruits and vegetables
five or more times a day compared to females. These results are also different from other studies

90

where highest level of household educational attainment was utilized as a measure of SES that
found higher SES to be associated with healthier dietary behaviour in female adolescents (Pitel et
al., 2013; Sahingoz & Sanlier, 2011; Sichert-Hellert et al., 2011), since this study found that
males from households where the highest level of educational attainment was a bachelor’s
degree or higher were more likely to consume fruits and vegetables five or more times a day than
females. The literature reviewed within this study where relationships between adolescent sex
and FVC were discussed originated from countries other than Canada. The current results
suggest that the relationship between sex and adolescent FVC may differ in Canada from other
countries.
Opinion of Weight
This study’s findings suggest that the adolescent’s opinion of their weight may be a more
accurate measurement than BMI when investigating relationships between weight and dietary
behaviour. The study found that adolescents whose opinion of their weight was just about right
had a higher likelihood of consuming fruits and vegetables five or more times a day, compared to
those whose opinion of their weight was either under or over, however no significant relationship
was found between BMI and FVC. These results add to the discussion regarding the
appropriateness of using BMI as a measurement of healthy weight or overall health status, in
addition to the use of BMI as a measurement of the relationship between weight status and
dietary behaviour. Although the majority of studies use BMI as a measurement of weight status,
the appropriateness of using BMI as an outcome variable to measure healthy weight has been
criticized for its lack of ability to distinguish fat from muscle and the location of body fat (Bell et
al., 2018; Gutin, 2019). The significant relationship between a respondent’s opinion of their
weight and FVC supports initiatives that propose new measurements of health in relation to

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weight, such as the Health at Every Size (HAES) approach. The HAES approach encourages
healthy lifestyle through intuitive eating, body acceptance, and physical activity for movement
and health, instead of focusing on body weight, shape, or size (Penney & Kirk, 2015). However,
philosophies such as the HAES are relatively new (Penney & Kirk, 2015), as such further
research is needed to better understand the relationship between weight and dietary behaviour.
The use of variables such as opinion of weight, verses BMI, when investigating dietary
behaviour, such as FVC, offer an area for further study.

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Chapter 6 – Summary
This study aimed to answer the research question, what is the relationship between food
literacy and FVC among Canadian adolescents. The study found several key findings, and a
number of recommendations for clinical practice and future research initiatives. The following
chapter contains a discussion on the strengths and limitations of the study, implications for
practice and research, and a summary of key findings.
Strengths and Limitations
Study Strengths
This study draws from a large data set to help fill a gap within the growing literature on
food literacy. Presently, national survey data regarding adolescent food literacy is limited, and
within the literature reviewed, there was a notable gap related to studies that examined
adolescent food literacy within the Canadian population. There were also no studies identified
within the literature review that analysed the current data set. The CCHS collects data from a
large population across a vast geographical area to produce large samples that aid in the ability to
generalize study results to the larger population of Canadian adolescents.
Study Limitations
Despite the strengths of this study’s findings, it has a number of limitations, including the
cross-sectional design, time lapse between data collection and analyses, potential selection bias,
generalizability, gaps within the data collection, and limitations within the current study scope.
As a cross-sectional study, the associations between the selected food literacy variables
and FVC were assessed at the same point in time and are therefore limited in their ability to
identify causality or draw conclusions between food literacy attributes and FVC (Polit & Beck,
2017). However, the cross-sectional study results can serve to inform hypothesis generation for

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future studies (Polit & Beck, 2017), supporting more rigorous investigation of the relationship
between food literacy and dietary behaviour.
The study results represent the population estimates from 2013, and as such may not be
generalizable in 2021. However, the Canadian adolescent population has experienced lower
growth than the overall population and as such data collected in 2013 can be argued to remain
relevant in 2021. Since 2013 the Canadian population has increased by 7.9% (Macrotrends,
2021). Between 2013 and 2020 youth aged 10 to 14 years increased from 1,886,668 to
2,072,100, and those aged 15 to 19 years dropped from 2,154,873 to 2,100,865 (Statistics
Canada, 2020b). The combined growth represented by Canadian youth increased by 7.3%
between 2013 and 2020, representing a lower increase as compared to the overall population.
Furthermore, those aged 0-14 years of age represent 15.9% of the population in 2020, and this
representation is estimated to remain consistent, at between 15% and 16% by 2025 (Statistics
Canada, 2020a). Therefore, despite the time lapse between the collection of the data and the
analyses, the lower rate of population growth represented by youth can be argued to minimize
some of the population change estimated between 2013 and 2021. Furthermore, despite the age
of the data, there remains a lack of data collection representative of food literacy within the focus
population, and as such the study results remain useful in guiding future research and
programing initiatives.
Despite the efforts of Statistics Canada to weight responses, and the analyses being
conducted on bootstrapped samples, there remains an unavoidable potential for self-selection
bias. Person-level weighting for the rapid response sample were calibrated by province, by age
group, and by sex to ensure weights sum up to known population totals (Statistics Canada,
2013b). However, it is difficult to ascertain if the sample’s highest level of household

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educational attainment is aligned with national averages. National data related to the overall
Canadian population reports lower household educational attainment than this study’s sample at
the time of the data collection. According to the National Housing Survey in 2011, highest level
of household education attainment reported as secondary school was 25.6%, compared to 16.8%
of the study’s population, and a bachelor’s degree or higher was 13.4% compared to 37.2% of
the study population (Statistics Canada, 2011a). These variances identified may be related to the
limited study population of households where children 12-17 years reside and may represent a
population with higher education. However, this discrepancy may result in an
underrepresentation of adolescents from households where the highest level of educational
attainment is below a bachelor’s degree and therefore further study should look at adolescents
from lower educated households to see if this is in fact the case.
It should be noted that the Aboriginal population referred to within the study results are
only reflective of the survey respondent’s who self identify as Aboriginal and are not
representative of the overall Canadian Aboriginal population. Furthermore, generalizability of
the results to the respondent’s who identify as Aboriginal is limited as the CCHS FS2 excludes
Canadians living on Aboriginal reserves and settlements, living in two remote health regions of
Québec (Région du Nunavik and Région des Terres-Criesde-la-Baie-James), living in all three
Territories, institutionalized persons, and those who are full-time members of the Canadian
Forces (Statistics Canada, 2013b). As such, the study results can not be generalized to these
populations. Although no significant relationships were found between respondent’s who
identify as Aboriginal and FVC, when interpreting results attributed to the respondent’s who
identify as Aboriginal, these results are only applicable to Aboriginal persons not living on
reserves or Aboriginal settlements and therefore may not be reflective of all Aboriginal persons.

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Due to the sparseness of the data for some of the provincial samples, generalizability of
study results beyond the national level is also limited. Geographical factors, such as rural verses
urban, are also not evaluated within this study, limiting the ability of this study to ascertain
additional societal /external factors affecting FVC of Canadian adolescents. To better understand
factors affecting FVC within the Canadian context, future studies are needed to explore the
impact of geographical factors on dietary behaviours.
The ability of this study to make associations between food and nutritional knowledge
and FVC is limited to the data collected by the CCHS RFS2 survey. Respondents within the
survey were asked questions regarding eating habits, basic ingredients within meals, and number
of servings of fruits and vegetables, however there was no collection of food diaries to allow for
the quality of the reported ingredients and foods to be evaluated. Furthermore, the outcome
variable, FVCGTOT, does not differentiate between consumption of potatoes, which are
processed as a carbohydrate, and fruit juices, which potentially contain a higher sugar content
and lower fiber content than whole fruit. Although, there are variables available within the data
set that differentiate between types of fruits and vegetables consumed, the analyses of these data
were outside the scope of this study, and further studies are needed to analyze the quality of
adolescent FVC.
The CCHS used within this study collected the variable ‘sex’ as only ‘male’ or ‘female’,
and did not present other categories identifying sexual orientation, assigned sex at birth, or
gender identity (Statistics Canada, 2013c). Although the CCHS in 2019 created a category of
questions related to sex and gender, in which respondents were asked ‘what was your sex at
birth?’ and ‘what is your gender?’, this category is not available within the data set used within
this study (Statistics Canada, 2019). The current study uses the term sex to refer to male and

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female as identified within the data set, however it should be acknowledged that the term ‘sex’
remains ambiguous in definition.
Finally, this study found the adolescent respondent’s opinion of their weight vs BMI had
a significant relationship to FVC. Considering the controversy surrounding the appropriateness
of using the BMI as a measure of healthy weight, future studies should consider evaluating
alternative measurements of health as related to weight, including the use of a respondent’s
opinion of their weight.
Implications for Clinical Practice and Research
The identified limitations and gaps, despite restricting this studies’ ability to provide a
more comprehensive understanding of the relationship between adolescent food literacy and
dietary behaviours, serve to inform the development of programs that aim to improve food
literacy and or, dietary behaviours among adolescents. The study results also aid in the
development of future study and survey designs to measure the levels of food literacy more
accurately within the Canadian adolescent population, allowing for continued investigation into
the impact that food literacy has on dietary behaviour. As an emerging and rapidly expanding
construct, conceptualizations of food literacy have become more delineated since the data from
the CCHS FS2 was collected in 2013, hence there are a number of recommendations for future
survey and study designs to facilitate further research initiatives surrounding food literacy and
Canadian adolescents.
Food Skills
Study results found food skills that were multifaceted and reflective of several food
literacy attributes to have a positive relationship to adolescent FVC. These results suggest that
food literacy programs should aim to teach and evaluate food literacy attributes that build the

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more complex skills needed to be able to adjust a recipe, or to cook from basic ingredients,
including attributes of food and nutritional knowledge and self-efficacy.
Household Education and Income
The present study found that total household income and highest level of household
educational attainment did not follow patterns previously identified within the literature, where
increasing income or education were found to be positively associated with higher FVC (Bau et
al., 2011; Chartatos et al., 2018; Fleary et al., 2018; Garrido-Fernández et al., 2019; Ghasab et
al., 2017; Nagy-Pénzes et al., 2020; Pearson at al., 2009; Pitel et al., 2013; Sahingoz & Sanlier,
2011). Therefore, future food literacy programs should aim to reach populations of all incomes
and household education levels.
Further research is needed to better understand the relationship between various types of
household educational attainment and FVC, including what barriers these populations may
encounter and how to best support adolescents from these households to engage in healthy eating
behaviours. To better understand the impact of total household income and the highest level of
household education within the Canadian context, geographical factors warrant further
exploration, including rurality vs CMA’s.
The expansion of the data set to include the highest level of educational attainment for
each adult caregiver within the home would aid in evaluating the impact of highest level of
household educational attainment by sex. Since the highest level of household education is
increasingly being used as a measure of SES within the literature, this variable should be
considered for weighting within the CCHS design.

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Food Literacy Framework
Future study and survey designs should aim to incorporate available food literacy
frameworks to guide study investigation and survey design. The food literacy framework by
Thomas et al. (2019) offers a format from which to base future food literacy questionnaires,
ensuring that data is collected for attributes from each food literacy category, allowing for a more
comprehensive evaluation of the relationships between food literacy and dietary behaviour. The
working group that is responsible for the development of the above mentioned framework
discussed in their paper the potential development of a tool suitable for food literacy program
evaluation that may also be utilized in future data collection efforts (Thomas et al., 2019).
Considering the variables found to have significant relationships with FVC within this study
involved capacity in a number of food literacy categories, future survey designs aimed at
understanding the influencing factors to adolescent diet behaviours should aim to incorporate
questions that evaluate all categories and attributes within the Thomas et al. (2019) framework.
Data Collection of Processed/ Snack Foods
Data collected on adolescent dietary behaviours should be expanded to include the
consumption of savory, sweet, and processed foods to better understand the eating habits of
adolescents. A number of studies have found positive correlations between food literacy
characteristics, such as food and nutritional knowledge (Bailey et al., 2019; Fleary et al., 2018;
Lake et al., 2004; Vaitkeviciute et al., 2014; Souza Santos et al., 2019) and societal/ external
factors (Bau et al., 2011; Fleary et al., 2018; Ghasab et al., 2017; Gracey et al., 1996; NagyPénzes et al., 2020; Park et al., 2013; Pearson et al., 2009; Pitel et al., 2013; Videon & Manning,
2003), to the lower consumption of savory, sweet, and processed foods in addition to higher
FVC. Considering the consumption of highly processed foods has been linked to increased

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obesity rates that potentially lead to lower health outcomes (Crino et al., 2015; Djupegot et al.,
2017; Holman & White, 2011; Lam & Adams, 2017; Nagle et al., 2015; IDRC, 2011, Poirier et
al., 2019; Whiteman & Wilson, 2016; WHO, 2002), these foods warrant inclusion in future
survey designs.
Availability of FVC
A significant barrier to healthy eating, identified by adolescents within the literature, was
the availability of fruits and vegetables within the home environment (Brooks & Begley, 2014;
Ghasab et al., 2017; Gracey et al., 1996), as such future surveys should consider adding
questions related to the availability of fruits and vegetables.
Parental Food Literacy
Additionally, both the primary caregiver within the household responsible for meal
preparation, as well as the adolescent should be interviewed. Parents have a significant influence
over the early food environment (Ghasab et al., 2017; Hanson et al., 2004; Mokhtari et al., 2017;
Orehek & Ferrer, 2018), therefore understanding how their food literacy impacts the home food
environment may aid in understanding the relationships between parental food literacy and
adolescent food literacy, and as such potentially inform future food literacy program
development. Within the current data set, one individual from each residence is selected to
respond, therefore associations between parental food literacy and the dietary behaviours of
adolescents are limited.
Summary and Conclusions
A general ‘de skilling’ among adolescents with respect to food choices and the
preparation of and consumption of foods associated with a healthy diet (Caraher &Lang, 1999, p.
9; Jaffe & Gartler, 2006; Slater & Mudryj, 2017) has been attributed to changing food practices

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within the home associated with a nutritional transition to a low-quality ultra-processed diet,
which require little food preparation skills (Monteiro et al., 2016; Slater & Mudryj, 2017). It has
been theorized that this shift has reduced the consumption of minimally processed foods such as
fruits and vegetables (Monteiro et al., 2016; Nelson et al., 2013; Slater & Mudryj, 2017). In
combination, the loss of food skills and knowledge, low quality food choices, and poor dietary
behaviours, put youth at risk of future chronic disease attributed to poor diet quality (Crino et al.,
2015; Djupegot et al., 2017; Holman & White, 2011; IDRC, 2011; Lam & Adams, 2017; Nagle
et al., 2015; Poirier et al., 2019; Whiteman & Wilson, 2016; WHO, 2002). Developing food
knowledge, skills and food related self-efficacy in adolescence may help better support these
individuals to make healthier informed food choices, limit their consumption of ultra-processed
foods, and thereby improve their health.
Food skills and food knowledge are core attributes of food literacy (Thomas et al., 2019),
which has been found to help shape adolescent eating behaviours (Vaitkevicicute et al., 2014).
Food literacy represents the interdependent concepts related to the facets influencing dietary
practices and as such has emerged as a concept to describe the “everyday practicalities associated
with healthy eating (Vidgen & Gallegos, 2014, p.50), and has garnered substantial attention
within the literature and in program development aimed at understanding factors affecting diet
decisions and at improving dietary behaviours (Truman et al., 2017).
This study’s aim was to examine the relationship between food literacy and FVC among
Canadian adolescents, through a secondary analysis of the Canadian community health survey,
rapid response on food skills (Part 2) – mechanical skills and food conceptualization. Significant
relationships between FVC and food literacy were evident in food skills that were multifaceted,
requiring that adolescents have the ability to perform a number of basic food skills and reflective

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of several food literacy attributes. Performance of the multifaceted food skills reflects a level of
food and nutritional knowledge and cooking self-efficacy, further emphasizing the
interconnected and reciprocal nature of food literacy described by Thomas et al. (2019). These
results suggest that food literacy programs should aim to teach and evaluate food literacy
attributes that build the more complex skills needed to be able to adjust a recipe, or to cook from
basic ingredients, including attributes of food and nutritional knowledge and self-efficacy.
Total household income, the highest level of household educational attainment,
geography, sex, and an adolescent’s opinion of their weight were all found within the current
study to have significant relationships to an adolescent’s likelihood of consuming fruits and
vegetables five or more times a day. However, some of the relationships found between the
societal/ external factors and adolescent FVC within this study were inconsistent with those
identified in previous studies, were increasing levels of household income (Fleary et al., 2018;
Ghasab et al., 2017) and household educational attainment were associated with healthier dietary
behaviours, including increased FVC (Bau et al., 2011; Chartatos et al., 2018; Fleary et al., 2018;
Garrido-Fernández et al., 2019; Ghasab et al., 2017; Nagy-Pénzes et al., 2020; Pearson at al.,
2009; Pitel et al., 2013; Sahingoz & Sanlier, 2011). This study found that adolescent FVC was
not associated with increasing levels of total household income or highest level of educational
attainment, instead the highest household income and educational categories were only found to
be predictive of higher adolescent FVC when compared to the lower middle income category of
$29,999-$60,000 and the highest level of household educational category of trades. The study’s
analyses of the relationship between household educational attainment and adolescent FVC
suggest that not all levels of post secondary education are associated with higher adolescent

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FVC. Study results pertaining to household education and income suggest future food literacy
programs should be inclusive of adolescents from any SES.
Other interesting study findings pertain to sex and opinion of weight. This study found
male adolescents to be more likely to consume fruits and vegetables five or more times a day
compared to females in the logistic regression analyses, contrary to previous findings were
females had higher FVC (Bailey et al., 2019; Ghasab et al., 2013; Park et al., 2013; Pearson et
al., 2009; Ronto et al., 2016; Sahingoz & Sanlier, 2011). Also, while an adolescent’s opinion of
their weight was associated with FVC, BMI was not, these findings add to the discourse within
the literature regarding the lack of validity in using BMI as a measure of healthy dietary
behaviour.
Key study findings suggest that higher levels of adolescent food literacy, as reflected in
multifaceted food skills that are reflective of individual capacity in several food literacy
categories, has the potential to positively impact their FVC. However, when societal/ external
factors are controlled for, food skills were not found to have a significant relationship with
adolescent FVC, suggesting that factors outside of the control of the individual have the potential
to minimize the influence of individual food literacy characteristics on adolescent FVC.

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Appendix A - Copyright
ELSEVIER LICENSE
TERMS AND CONDITIONS
Jan 14, 2021
This Agreement between Mrs. Sophia Mattioli ("You") and Elsevier
("Elsevier") consists of your license details and the terms and conditions
provided by Elsevier and Copyright Clearance Center.
License Number

4986850126470

License date

Jan 12, 2021

Licensed Content
Publisher

Elsevier

Licensed Content
Publication

Journal of the Academy of Nutrition and
Dietetics

Licensed Content Title

Complexities in Conceptualizing and
Measuring Food Literacy

Licensed Content Author

Heather Thomas,Elsie Azevedo Perry,Julie
Slack,H. Ruby Samra,Ella Manowiec,Lisa
Petermann,Elizabeth Manafò,Sharon I.
Kirkpatrick

Licensed Content

Date Apr 1, 2019

Licensed Content Volume

119

Licensed Content Issue

4

Licensed Conten Pages

11

Start Page

563

End Page

573

Type of Use

reuse in a thesis/dissertation

116

Portion

figures/tables/illustrations

Number of
figures/tables/illustrations

1

Format

both print and electronic

Are you the author of this
Elsevier article?

No

Will you be translating?

No

Title

How does food literacy affect fruit and
vegetable consumption and body mass index
among Canadian adolescents?

Institution name

University of Northern British Columbia

Expected presentation
Date

Apr 2021

Portions

Figure 4. Food Literacy Framework. page 570
Mrs. Sophia Mattioli
1702 Crystal Street

Requestor Location
Quesnel, BC V2J4R8
Canada
Attn: Sophia Mattioli
Publisher Tax ID

GB 494 6272 12

Total

0.00 CAD

Limiters or Filters

Conducted - May 2018,
Updated - November 2020

Narrative of search:

Data Base searched

Subject Age - adolescent: 1318 years
Source – Academic Journal
Language - English

S1 - "food literacy AND
(adolescents or teenagers or
teens or young people)"

S2 - "nutritional behavior
AND adolescents"

S3 - "nutritional knowledge
AND adolescents"

S4 - "food literacy" (MH
"Food Preferences") OR (MH
"Portion Size") OR (MH "Food
Habits") OR (MH "Eating
Behavior") AND adolescent
AND home environment AND
skill AND knowledge

CINAHL Ebsco

Medicine and Dental; Nursing
and health professionals

"nutritional behavior"
OR "food habits"
AND adolescent
AND "home environment"
AND skill
AND "Nutritional knowledge
"OR "food literacy”

Science Direct

Appendix B - Narrative Description of Literature Search

English
adolescent: 13-18 years
Humans

((((('nutritional AND
knowledge')
OR ('food AND skills'))
OR ('food AND literacy'))
OR (diet OR food AND 'self
AND efficacy'))
OR ('dietary AND behaviour'
OR 'food AND decisions' Or
'food AND choice'))

PubMed Medline

117

118

Appendix C - Literature Search Inclusion and Exclusion Criteria
Inclusion
➢ Scholarly/ peer reviewed/ English
➢ Articles that included participants
between 12 and 17 years of age
➢ Articles that included a discussion of
food literacy or food literacy attributes

Exclusion
➢ Studies that did not include data
collected from adolescents
➢ Articles that focus on specific
population including any of the
following:
o Athletes
o Pre and peri natal populations
o Populations where ecological
factors have greater influence
than food literacy as a concept,
such as severe food insecurity,
rural or tribal Indian, Ethiopia,
homelessness, or refugees
o Populations affected a chronic
health conditions, nutritional
disorder or syndrome, or other
disease or chronic condition,
including ethanol, diabetes,
malnourishment, or cancer
➢ Articles that focused on oral health
➢ Articles that focused on supplements
➢ Articles that focused on weight loss
interventions
➢ Articles that focused on micronutrient

- recategorized

DHH_AGE

(Based on FVCDJUI, FVCDFRU, FVCDSAL, FVCDPOT,
FVCDCAR, FVCDVEG. The CCHS measures the number of
times (frequency), not the amount consumed)
Age

DHH_SEX
DHHDHSZ

DHHDLVG

Sex

Household size

Number of persons
Living arrangement of selected respondent

(“What is your age?)

FVCDTOT

FVC

FVCGTOT

Daily consumption – total fruits and vegetables

(How many servings of other vegetables do you usually eat?)

Fruit and vegetable consumption

(How many servings of other vegetables do you usually eat?)

Fruit and vegetable consumption

Categorical

Continuous

Categorical; nominal

Continuous

Continuous; ratio

Categorical – recoded *

Categorical; Ordinal
Dependent variable

1-99

0-12

1-2

Years
12-105

0.0999.9

0-1

1-3

1- Unattached individual living
alone
2- Unattached individual living
with others

Data set will be drawn only for
respondents aged 12-17 years of
age.
Male – 1
Female - 2
0-12

Continuous ratio:
Years between 12-17

1- Eats fruits and vegetables less
than 5 times per day
2- Eats fruits and vegetables less
than 5 times per day
3- Eats fruits and vegetables
more than 10 times per day
9 - Not stated
0 - Eats fruits and vegetables less
than 5 times per day
1 -Eats fruits and vegetables 5
times per day or more
0.0-24.2 – Times per day
999.9 – Not stated

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

Appendix D - Variables Table

119

SDCDABT

SDCFIMM

Gen_01

Aboriginal Identity

Immigrant

Self-perceived health

Self-Perceived health

(In general, would you say your health is... )

lvg

Living arrangement of selected respondent – recategorized

Categorical – recoded *

Categorical

Categorical

Categorical

Categorical -recoded *

1-2

1-9

1-9

1-3

3- Living with spouse/ partner
4- Parent living with spouse/
partner and children
5- Single parent living with
children
6- Child living with a single
parent
7 – Child living with single
parent, siblings
8 - Child living with two parents
9 - Child living with two parents,
siblings
10 - Other
99 - not stated
1 - child not living with parent
2 - child living with single parent
3 - child living with 2 parents
1 – Aboriginal (First nations,
Metis, Inuit)
2 – Not Aboriginal
6 – Not applicable
9 – Not stated
1 – Yes
2 – No
9 – Not stated
1 - Excellent
2 - very good
3 - Good
4 - Fair
5 - Poor
7 – Don’t Know
1 – Very good or excellent
2 – Good, fair, or poor

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

120

BMI

FS2_**
FS2_10

BMI

Food skills

Eating habits

Eating habits

Fs2eh2

HWTDCOL

BMI class. (12 to 17)

In general, would you say that your eating habits are:

weightop

HWT_4

Respondent's opinion of own weight - self-reported

(Do you consider yourself?)

Respondent's opinion of own weight - self-reported

Categorical – recoded *

1-2

1-9

1-2

Categorical – recoded *

Categorical

1-9

1-2

1-9

Categorical

Categorical – recoded *

Categorical

1 - Excellent
2 - Very good
3 - Good
4 - Fair
5 - Poor
7 – Don’t Know
8 – Refused
9 – Not stated
1 – Very good or excellent (1-2)
2 – Good, fair, or poor (3-5)
Missing – (7-9)

Missing – (7)
1-Overweight
1-Underweight
3- Just about right
6- Not applicable
7- Don’t Know
8- Refusal
9- Not stated
1 – Just about right
2 – under or over
Missing – Don’t know, refused, or
not stated
1-Neither overweight nor obese
2-Overweight
3-Obese
6- Not applicable
9- Not stated
1 - Neither overweight nor obese
2 - Overweight or obese
Missing – (6-9)

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

121

FS2_30

Ability to cook - basic ingredients

How would you describe your ability to cook from basic
ingredients?
Would you say:

Fs2mm2

FS2_20

Main meal

When preparing the MAIN meal at home, which of the
following
^YOURFAMILY do the most often? By main meal we mean
the meal of
the day that requires the most preparation.

Main Meal at home

Categorical

Categorical – recoded *

Categorical

1-9

1-2

1-9

1 - You use mostly whole basic
foods such as vegetables,
fruits, pasta, legumes and
meat
2 - You use mostly easy to
prepare foods such as frozen
lasagna
3 - You use a mix of whole
basic foods and easy to prepare
foods
4 - You buy ready-to eat food
or order takeout or delivery
7 – Don’t know
8 – Refused
9 – Not stated
1 - Mostly whole foods (1)
2 - Not mostly whole foods (2-4)
Missing – (7-9)
1 - I don’t know where to start
when it comes to cooking
2 - I can do things such as boil
an egg or cook a grilled cheese
sandwich but nothing more
advanced
3 - I can prepare simple meals
but nothing too complicated
4 - I can cook most dishes if I
have a recipe to follow
5 - I can prepare most dishes
6 - I frequently prepare
sophisticated dishes
97 – Don’t know
98 – Refused

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

122

FS2_40

Skills - Using a kitchen knife safely

FS2_50

Skills - Peeling, chopping, slicing vegetables or fruit

Categorical

FS2_60

Skills - Freezing veg. or fruit from raw in home freezer

(How would you rate your skills:)
in freezing vegetables or fruit, from raw to bagged in your
home freezer?

Categorical – recoded
*

Fs2pcs2

Categorical

Categorical – recoded *

Categorical

Categorical – recoded *

Skills - Peeling, chopping, slicing

How would you rate your skills:
in peeling, chopping or slicing vegetables or fruit?

Fs2kn2

Skills – Knife

How would you rate your skills:
in using a kitchen knife safely?

Fs2bi2

Cook from basic ingredients

1-9

1-2

1-9

1-2

1-9

1-2

1 - Very good
2 - Good
3 - Basic
4 - Very limited / No skills
7 – Don’t Know
8 – Refused
9 – Not stated

1 – High (1-2)
2 – Low (3-4)
Missing – (7-9)

2 – Low (3-4)
Missing – (7-9)
1 - Very good
2 - Good
3 - Basic
4 - Very limited / No skills
7 – Don’t Know
8 – Refused
9 – Not stated

1 – High (1-2)

99 – Not stated
1 – High (3-6)
2 – Low (1-2)
Missing – (97-99)
1 - Very good
2 - Good
3 - Basic
4 - Very limited/ No skills
7 – Don’t Know
8 – Refused
9 – Not stated

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

123

FS2_70

Canning from raw in sealed glass jars

Skills – Stew, Soup, Casserole

Fs2soup2

FS2_90

Skills - Soup, stew or casserole from scratch

How would you rate your skills:
in cooking a soup, stew or casserole from scratch?

Fs2mt2

Skills – Meat

Categorical – recoded
*

Categorical – recoded
*

Categorical

FS2_80

Cooking piece of raw meat/chicken/fish

(How would you rate your skills:)
in cooking a piece of raw meat/chicken/fish?

Categorical – recoded
*

Fs2ca2

Categorical

Categorical – recoded
*

Skills – Canning

(How would you rate your skills:)
in canning from raw ingredients to finished products in
sealed glass jars?

Fs2fr2

Skills – Freezing

1-2

1-9

1-2

1-9

1-2

1-9

1-2

1 – High (1-2)
2 – Low (3-4)

1 - Very good
2 - Good
3 - Basic
4 - Very limited / No skills
7 – Don’t Know
8 – Refused
9 – Not stated

1 – High (1-2)
2 – Low (3-4)
Missing – (7-9)

2 - Good
3 - Basic
4 - Very limited / No skills
7 – Don’t Know
8 – Refused
9 – Not stated

1 – High (1-2)
2 – Low (3-4)
Missing – (7-9)
1 - Very good

1 - Very good
2 - Good
3 - Basic
4 - Very limited / No skills
7 – Don’t Know
8 – Refused
9 – Not stated

1 – High (1-2)
2 – Low (3-4)
Missing – (7-9)

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

124

FS2_110

Skills - baking muffins or cake from scratch with a recipe

FS2_120

Adjust a recipe to make it healthier

Have you ever adjusted a recipe to make it healthier?

Fs2scratch2

Skills – Bake from scratch, recipe

(How would you rate your skills:)
in baking muffins or cake from scratch with a recipe?

Fs2mix2

FS2-100

Skills – Baking from mix

(How would you rate your skills:)
in baking muffins or cake using a pre-packaged mix?

Skills - baking muffins/cake using pre-packaged mix

Categorical

Categorical – recoded
*

Categorical

Categorical – recoded
*

1-9

1-2

1-9

1-2

1-9

If FS2_Q120 =1, go to
FS2_Q130.
Otherwise, go to FS2_Q140.

1 - Yes
2 - No
7 – Don’t know
8 – Refused
9 – Not stated

1 – High (1-2)
2 – Low (3-4)
Missing – (7-9)

1 - Very good
2 - Good
3 - Basic
4 - Very limited / No skills
7 – Don’t Know
8 – Refused
9 – Not stated

1 – High (1-2)
2 – Low (3-4)
Missing – (7-9)

1 - Very good
2 - Good
3 - Basic
4 - Very limited / No skills
7 – Don’t Know
8 – Refused
9 – Not stated

Missing – (7-9_

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

125

INCDHH

Total household Income from all sources

FS2_140

Added more vegetables or fruit
Grow vegetables, herbs or fruits in home or community
garden

GEO_PRV

Fs2addfv

How did you make it healthier?

Province of residence of respondent

FS2_130D

Made it healthier - added more veg. or fruit

FS2_140

FS2_130

How did you make it healthier?

When season permits, do you grow vegetables, herbs, or
fruits at home or in a community garden?
Garden

FS2_120

Adjust a recipe, healthier

Categorical; nominal

Categorical; nominal

Categorical – recoded *

Categorical

Categorical – recoded
*

Categorical

Categorical – recoded
*

1-15

10-59

1-2

1-9

1-2

1-9

1-2

10 - Newfoundland and Labrador
11 - Prince Edward Island
12 - Nova Scotia
13 - New Brunswick
24 - Quebec
35 - Ontario
46 - Manitoba
47 - Saskatchewan
48 - Alberta
59 - British Columbia
1 - NO INCOME
2 - LESS THAN $5,000
3 - $5,000 TO $9,999

1 – Yes
2 - No
Missing – (7-9)

2 - No
Missing – (7-9)
1 - Yes
2 - No
7 – Don’t know
8 – Refused
9 – Not stated

1 – Yes

1 - Yes
2 - No
7 – Don’t know
8 – Refused
9 – Not stated

1 – Yes
2 – No
Missing –(7-9)

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

126

Inc4

EDUDH10

Total household income from all sources

Highest level of education – household, 10 types

Categorical

Categorical – recoded *

1-9

1-4

4 - $10,000 TO $14,999
5 - $15,000 TO $19,999
6 - $20,000 TO $29,999
7 - $30,000 TO $39,999
8 - $40,000 TO $49,999
9 - $50,000 TO $59,999
10 - $60,000 TO $69,999
11 - $70,000 TO $79,999
12 - $80,000 TO $89,999
13 - $90,000 TO $99,999
14 - $100,000 TO LESS THAN
$150,000
15 - $150, 000 OR MORE
1 - <$30,000
2 - $30,000-$59,999
3 - $60,000-$89,999
4 - >$90,000
1 – Grade 8 or lower (Que. Sec ii
or lower)
2 – Grade 9-10 (Que. Sec iii, iv;
NFLD sec 1)
3 – Grade 11-13 (Que. Sec v;
NFLD sec2-3)
4 – Secondary school grade., no
post sec
5 – Some post secondary
6 – Trade’s certificate or diploma
7 – Diploma/ certificates –
college/ CEGEP
8 – Univ. Certificate below
bachelor’s level
9 – Bachelor’s degree

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

127

* Recoded variables as used in data analyse.

conceptualization, data set (Statistics Canada, 2013c).

10 – Univ. degree or cert. above
bac. Level
99 – Not stated
Highest level of education – household, 6 types –
Edudh06
Categorical – recoded *
1-6
1 - Less than secondary school
recategorized
graduation (1-3)
2 - Secondary school graduation
(4)
3 - Some post-secondary (5)
4 – Trades (6)
5 - Certificate/ diploma/ <
bachelor’s degree (7-8)
6 - ≥Bachelor’s degree (9-10)
Missing – (99)
Note: Variables represented in table are from the Canadian community the health survey, rapid response on food skills (Part 2) – mechanical skills and food

Canadian Community Health Survey– Rapid Response on Food Skills (Part 2) – Mechanical Skills and Food
Conceptualization, 2013
Concept:
Related
Type of variable
Range Description of the variable
variables of
Values - Categories
interest

128