Interventions to Support System-level Implementation of Health Promoting Schools: A Scoping Review
Bibliographic record
Abstract
Health promoting schools (HPS) is recognized globally as a multifaceted approach that can support health behaviours. There is increasing clarity around factors that influence HPS at a school level but limited synthesized knowledge on the broader system-level elements that may impact local implementation barriers and support uptake of a HPS approach. This study comprised a scoping review to identify, summarise and disseminate the range of research to support the uptake of a HPS approach across school systems. Two reviewers screened and extracted data according to inclusion/exclusion criteria. Relevant studies were identified using a multi-phased approach including searching electronic bibliographic databases of peer reviewed literature, hand-searching reference lists and article recommendations from experts. In total, 41 articles met the inclusion criteria for the review, representing studies across nine international school systems. Overall, studies described policies that provided high-level direction and resources within school jurisdictions to support implementation of a HPS approach. Various multifaceted organizational and professional interventions were identified, including strategies to enable and restructure school environments through education, training, modelling and incentives. A systematic realist review of the literature may be warranted to identify the types of intervention that work best for whom, in what circumstance to create healthier schools and students.
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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 | 0.029 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".