<i>Meanings That Youth Associate</i>With Healthy and Unhealthy Food
Bibliographic record
Abstract
PURPOSE: The symbolic meanings that youth associate with food were explored, as were barriers to accessing healthy foods. METHODS: Qualitative methods and a constructivist approach were employed, and data were collected through semi-structured interviews and a card-sorting activity. Thirteen adolescents aged 13 to 15 (seven girls, six boys) were recruited through public schools and posters displayed in community settings. Thematic analytical techniques were used to analyze the data. RESULTS: Participants classified foods into healthy and unhealthy groups, as well as into an "in-between" group that included nutritionally enhanced foods. Healthy and unhealthy foods were linked to a variety of physical, social, and emotional meanings. Some meanings associated with foods were also discussed in gendered terms, and numerous barriers to accessing healthy foods were reported. CONCLUSIONS: Foods hold multiple meanings for youth. Programs and policies aimed at fostering healthy eating need to capitalize on positive associations related to healthy foods. Negative associations related to healthy foods need to be acknowledged and strategies developed to recast such linkages. Likewise, the positive associations linked to unhealthy foods need to be addressed. Strategies also need to be developed to ensure access to healthy foods in all settings, especially within schools and community leisure settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".