Health‐Promoting School Culture: How Do We Measure it and Does it Vary by School Neighborhood Deprivation?*
Bibliographic record
Abstract
BACKGROUND: The context in which school-based health-promoting interventions are implemented is key for the delivery and success of these interventions. However, little is known about whether school culture differs by school deprivation. METHODS: Using data from PromeSS, a cross-sectional study of 161 elementary schools in Québec, Canada, we drew from the Health Promoting Schools theoretical framework to develop four measures of health-promoting school culture (i.e., school physical environment, school/teacher commitment to student health, parent/community engagement with the school, ease of principal leadership) using exploratory factor analysis. One-way ANOVA with post-hoc Tukey-Kramer analyses was used to examine associations between each measure and social and material deprivation in the school neighborhood. RESULTS: Factor loadings supported the content of the school culture measures and Cronbach's alpha indicated good reliability (range: 0.68-0.77). As social deprivation in the school neighborhood increased, scores for both school/teacher commitment to student health and parent/community engagement with the school decreased. IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: Implementation of health-promoting interventions in schools located in socially deprived neighborhoods may require adapted strategies to address challenges related to staff commitment and parental and community involvement. CONCLUSION: The measures developed herein can be used to investigate school culture and interventions for health equity.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".