Evaluation of Social Impact Within Primary School Health Promotion: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Health promotion programs and interventions are designed to encourage behavioral changes in children, encouraging them to make safe and healthy life choices. This systematic review seeks to examine how social impact is measured in primary school health promotion interventions. METHOD: A systematic search and review process was used to identify and examine primary school health promotion interventions. The PRISMA guidelines were followed to source articles from 6 electronic databases reporting school health promotion programs or interventions in Australia, Canada, New Zealand, or the United Kingdom. RESULTS: A total of 77 studies were located, representing 55 health promotion interventions delivered in primary school settings. Of these interventions, only 8 (15%) measured or attempted to measure social impact, whereas another 8 (15%) alluded to social impact. The predominant theories reported were social based theories (theories which examine the social influences on people, environments, and behaviors) (n = 17, 59%), with almost a third not informed by an overt health promotion framework or model (n = 34, 59%). A systematic rating system identified some level of stakeholder engagement (n = 30, 53%). CONCLUSIONS: This systematic review highlights the need for social impact measurement within health promotion to illuminate the role of school programs in delivering lasting change.
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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 | 0.028 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".