Factors influencing the adoption of a Health Promoting School approach in the province of Quebec, Canada
Bibliographic record
Abstract
This study examined a prediction model that integrated three categories of predictors likely to influence adoption of the Quebec Healthy Schools (HS) approach, i.e. attributes of the approach, individual and contextual characteristics. HS receptivity was considered as a potential mediator. For this study, 141 respondents representing 96 schools participated in a postal survey. We used bivariate logistic regression to assess factors associated with HS adoption and Baron and Kenny's method to test the mediation effect of HS receptivity. Four predictors related to school organizational characteristics had more weight in influencing the adoption of HS: the 'presence of leaders within schools', 'perceived school contextual barriers', 'school investment in healthy lifestyles' and 'beliefs in collective efficacy'. The influence of the latter two predictors was not direct but mediated by HS receptivity. Our findings showed that standard attributes generally considered as predictors of the adoption of an innovation are not the strongest determinants to explain HS adoption in the present context. The results shed light on the crucial role of organizational context in the adoption of this type of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".