Correlates of perceived success of health-promoting interventions in elementary schools
Bibliographic record
Abstract
INTRODUCTION: School-based health-promoting interventions (HPIs) aim to support youth development and positively influence modifiable lifestyle behaviours. Identifying factors that contribute to or hinder the perceived success of HPIs could facilitate their adaptation, improve implementation and contribute to HPI sustainability. The objective of this study was to identify factors in three domains (school characteristics, characteristics of the HPI and factors related to planning and implementing the HPI) associated with perceived success of HPIs among school principals in elementary schools. METHODS: Data were drawn from Project PromeSS, a cross-sectional survey of school principals and/or nominated staff members in a convenience sample of 171 public elementary schools in Quebec, Canada. School board and school recruitment spanned three academic school years (2016-2019). Data on school and participant characteristics, HPI characteristics, variables related to HPI planning and implementation and perceived success of the HPI were collected in two-part, structured telephone interviews. Descriptive statistics were used to characterize schools and study participants. Twenty-eight potential correlates of perceived HPI success were investigated separately in multivariable linear regression modelling. RESULTS: Participants generally perceived HPIs as highly successful. After controlling for number of students, language of instruction, school neighbourhood and school deprivation, we identified five correlates of perceived success, including lower teacher turnover, higher scores for school physical environment, school/teacher commitment to student health, principal leadership and school being a developer (vs. adopter) of the HPI. CONCLUSION: If replicated, these factors should be considered by HPI developers and school personnel when planning and implementing HPIs in elementary schools.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".