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Record W3035832117 · doi:10.7939/r3-694w-ac88

Food for Thought: A Qualitative Study Exploring Food Skills Education as a Determinant of Healthy Eating

2020· article· en· W3035832117 on OpenAlexaboutno aff
Shelby Laine Johnson

Bibliographic record

VenueUniversity of Alberta Library · 2020
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyQualitative researchFood scienceDevelopmental psychologySocial psychologySociologySocial science

Abstract

fetched live from OpenAlex

Background: Canada is witnessing a growing recognition on the importance of food literacy; knowing how to purchase, prepare, and eat healthy food. Previous research has shown these core competencies can contribute to healthy eating. This is supported with the recent inclusion of certain competencies in Canada’s updated Food Guide. Exposing children to food literacy opportunities can positively promote lifelong healthy eating behaviours and prevent risk of disease and obesity. Currently, Canadian youth have limited opportunities to develop their food skills at home due to women entering the work force and a growing reliance on convenience foods. As a result, schools have become a key secondary learning environment. It is subsequently a public health concern that junior high food skills education (FSE) courses in Alberta are voluntary. Research Purpose/Objectives: The research purpose of this paper-based thesis was to uncover whether junior high students and staff associate learning about nutrition and food skills with lifelong healthy eating behaviours. For Study 1: “Is Learning to Cook Optional?” an objective was to investigate how gender and perceived academic value may impact a youth’s perceptions and enrollment in food skills education courses. For Study 2: “Food Skills; an Academic Course?” an objective was to identify barriers and facilitators to the effective implementation of school-based food skills education courses. Methods: Focused ethnography was used as it allowed the researcher to explore specific research questions in a feasible timeline. Data generation consisted of semi-structured interviews with ten students, three principals, and three FSE teachers. Latent content analysis was the selected analytical tool. Through a cyclical process, primary patterns in the data were identified, coded, and categorized. Results: Study 1: “Is Learning to Cook Optional?” consisted of four main themes: Learning, Family, Gender, and Independence. Gender was significant as mothers remain primary educators for food skills and female students felt pressured to enroll in FSE courses. In Study 2: “Food Skills; an Academic Course?”, four main categories emerged: Promoting Healthy Eating, Curriculum, Job Qualifications, and Budget. Participants often recognized the value of FSE courses but identified budget limitations and scheduling restrictions as barriers to sustainable implementation. Promoting healthy eating was considered a school responsibility which resulted in support for the incorporation of further healthy eating initiatives. Implications: This research project had implications for the professions of both health promotion and education. A recommendation for practice includes schools encouraging teachers with a limited background in food literacy to attend professional development opportunities. School policies may need to be adapted to ensure an equitable distribution of funds beyond core courses. Health promotors should ensure the youth perspective is considered when developing healthy eating initiatives to enhance their potential for success. This research project also promotes a community-based approach when developing and implementing health promotion initiatives. The impact of academic prioritization and role of parents were identified in both studies as barriers that require further research.

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)0.010
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0140.012
Scholarly communication0.0060.004
Open science0.0020.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.043
GPT teacher head0.293
Teacher spread0.249 · 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 designQualitative
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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