Examining the context of health promoting schools: a translational approach to characterization and measurement of school ethos to support health and wellbeing
Bibliographic record
Abstract
Background: Health promoting schools (HPS) is hypothesized to influence student health and wellbeing by promoting a ‘school ethos’ that shapes the physical environment, social relations, organisational structure, policies and practices within the school. This complex set of conceptual dimensions makes school ethos challenging to measure as an important context for the implementation of HPS. The purpose of this research was to develop and explore a comprehensive measure of health promoting school ethos (HPSE) for the evaluation of HPS implementation, student health and well-being. Methods: We used a multi-method, iterative process to identify relevant HPSE concepts through triangulation of conceptual literature, existing tools and the tacit knowledge of school stakeholders. The HPSE measurement tool was administered to 18 elementary schools through a principal and teacher survey and an environmental assessment, followed by the development of a total and dimensional HPSE scores for each school. Testing for internal consistency of items was used to examine theorised concepts and sub-scores across HPSE dimensions, and total scores are summarised. Results: HPSE included eight conceptual dimensions with internal consistency ranging from α = 0.60 to a = 0.87. Total HPSE scores across schools ( N = 18) ranged from 1 to 8 ( Mean = 3.94, SD = 2.1), with 28% to 65% of schools reporting ‘high’ on respective HPSE dimensions. Conclusions: The HPSE tool holds potential for the conceptualization of critical components of school context as it relates to HPS. Schools included a heterogeneous mixture of health supportive school ethos, particularly among sub-dimensions.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".