Engaging Youth In Creating A Healthy School Environment: A Photovoice Strategy
Bibliographic record
Abstract
This study examined a pilot participatory needs assessment that was conducted with nine senior high school students from Port of Spain, Trinidad. Photovoice was used to engage these students in critical dialogue about their perceptions of issues affecting their health. Trained graduate students facilitated a 3-day training session in photovoice technique/ethics, writing narratives, critical reflection and dialogue with these students. Once trained, they were given disposable cameras and asked to photograph their school environment and document their thoughts on what they had photographed. After collation of photos and dialogue, seven health themes emerged. The most recurring themes included quality of the food served at schools, need for safe, clean and well-maintained school facilities, and role modeling by teachers, parents and community. Recommendations to address the concerns identified were discussed by the participants. The study concluded that conducting needs assessment, which concentrates on the voices of those affected, can be a first step in creating successful and cost-efficient programs and interventions tailored to specific groups. A needs assessment using photovoice should be a technique considered by school staff, government leaders, health professionals, and NGOs.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".