A translational approach to characterization and measurement of health-promoting school ethos
Bibliographic record
Abstract
A health promoting schools (HPS) approach is hypothesized to influence student health and wellbeing by promoting a 'school ethos' that reflects the physical environment, social relations, organisational structure, policies and practices within schools. This complex set of factors makes health promoting school ethos (HPSE) challenging to define and measure. This work sought to theorise, develop and pilot a measure of HPSE as the context for implementation of HPS initiatives. 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 HPSE scores for each school. Testing for internal consistency of items was used to examine theorized concepts, and scores for each school are summarised. HPSE included eight conceptual dimensions with internal consistency ranging from α = 0.60 to α = 0.87. Total HPSE scores across schools (N = 18) ranged from 1 to 8 (mean = 3.94, SD = 2.1), with 28-65% of schools reporting 'high' on respective HPSE dimensions. Schools included a heterogeneous mixture of HPSE scores, particularly across different dimensions. Our novel approach to tool development allowed us to conceptualize HPSE using a flexible process comprising different types and sources of evidence. The HPSE tool holds potential for identification and measurement of critical components of different school context as it relates to HPS.
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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 | 0.049 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".