Equalizing the Playing Field and Improving School Food Literacy Programs Through the Eyes of Teens: A Grounded Theory Analysis Using a Gender and Sport Participation Lens
Bibliographic record
Abstract
Background: School food literacy programs (e.g., home economics) are an opportunity to improve the dietary habits of teens. However, the literature suggests that girls and athletes have better food literacy, and it is not clear how school programs contribute to this inequality. To address this, we explored how gender and sport influenced teens’ perspectives of their school food literacy experiences and how they can be improved. Methods: Using semi-structured interviews and a Grounded Theory analysis, we generated a theoretical understanding of how to improve school food literacy programs for athletes and non-athletes of diverse genders. Thirty-three teens were recruited to balance sport participation (n = 18 athletes) and gender (n = 15 boys; n = 14 girls; n = 4 non-binary) based on data saturation. Results: Teens expressed four categories to improve school programs that aligned with principles of the Capability, Opportunity, Motivation and Behaviours (COM-B) Model of behaviour change. Programs should Provide a challenge (e.g., more advanced recipes), Make it fun (e.g., explore new cuisines in interactive ways) and Establish importance (e.g., health impacts). Practice is key for teens’ self-confidence and development of food skills (e.g., meal planning) as well. Boys emphasized Make it fun whereas girls and non-binary teens emphasized Establishing importance. Athletes valued Practice is key more than non-athletes. Conclusions: School programs should relay the importance of food literacy in fun and tailored ways to teens (e.g., meal planning among athletes). It may be especially salient for programs to tailor their activities and messaging, where possible, to appeal to diverse teens who play sports and those who do not.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".