Food in Focus: Youth Exploring Food in Schools Using Photovoice
Bibliographic record
Abstract
OBJECTIVE: As part of a study exploring school food environments, this study aimed to understand youth perspectives of school food. DESIGN: Photovoice, a qualitative visual methodology, was used to engage participants through photo-taking, with goals of enabling reflection, promoting dialogue, and facilitating change. SETTING: Participants were recruited through 2 youth-focused community organizations in Nova Scotia, Canada. PARTICIPANTS: Seven youths took part: 3 from a rural area and 4 from an urban center. PHENOMENON OF INTEREST: Youth perspectives on school food environments. ANALYSIS: The photovoice process of selecting, contextualizing (using the SHOWeD method), and codifying was used for analysis. RESULTS: Four themes were identified. First, spaces and places were important to youth food experiences. Second, key components of food environments were identified as quality, variety, time, and price. Third, the relation between food and social influence was highlighted. Fourth, the importance of amplifying youth voice was discussed. CONCLUSIONS AND IMPLICATIONS: Youth emphasized a desire for greater variety and quality in affordable school food options and the opportunity to be involved in decision-making regarding school food. Future research in other contexts and across larger samples is warranted to extend these findings to help inform stakeholders in school food policy and program implementation.
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 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".