The picture of health: examining school-based health environments through photographs
Bibliographic record
Abstract
Health-promoting schools (HPS) is an effective approach to enhance the health and well-being of children and youth, but its measurement remains a challenge considering contextual differences across school environments. The purpose of this study was to qualitatively explore the physical features of the school environment through photographs of schools that had implemented an HPS approach compared with schools that had not. This study used a descriptive approach, wherein physical features of the school environment were distilled through visual images and qualitatively analyzed. School environment data were collected from 18 elementary schools (10 HPS, 8 comparison schools) from a school board in rural Nova Scotia (Canada). Evaluation assistants captured photographs of the physical school environment as part of a broader environment audit. Overarching themes included the promotion, access and availability of opportunities for healthy eating and physical activity, healthy school climate and safety and accessibility of the school. The photographs characterized diverse aspects of the school environment and revealed differences between schools that had implemented an HPS approach compared with schools that had not. There were increased visual cues to support healthy eating, physical activity and mental well-being, and indications of a holistic approach to health among schools that implemented an HPS approach. This research adds to understanding the environmental elements of HPS. The use of photographic data to understand school environments provided an innovative method to explore the physical features of schools that had implemented an HPS approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".