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Record W4212957088 · doi:10.24124/2021/59198

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

2021· dissertation· en· W4212957088 on OpenAlexaffabout
Sophia Mattioli

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsConsumption (sociology)Affect (linguistics)Food choiceEnvironmental healthPopulationLiteracyHealth literacyMedicinePsychologyHealth carePedagogyPolitical science

Abstract

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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 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.261
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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