Schools' absorptive capacity to innovate in health promotion
Bibliographic record
Abstract
PURPOSE: A comprehensive "health promoting schools" (HPS) approach is advocated by the World Health Organization to foster the health of students. To date, few studies have evaluated schools' capacity to implement it in an optimal way. The purpose of this paper is to present a conceptual framework that identifies core features likely to facilitate the incorporation of innovation, such as HPS, into school functioning. DESIGN/METHODOLOGY/APPROACH: The framework was built by combining dimensions derived from two major strands of literature, i.e. management and HPS. It has taken root in Zahra and George's model of organisation absorptive capacity (AC) for new knowledge but has been adapted to better explore AC in a school context. The contrasting cases of two secondary schools that adopted a HPS approach in Quebec, Canada, for at least three years were used to illustrate the value of the framework. FINDINGS: The framework proposed is a multidimensional model that considers components such as modulators, antecedents, integration mechanisms and strategic levers as potential determinants of AC, i.e. acquisition, assimilation, transformation and exploitation. The conceptual framework helped to qualify and compare AC regarding HPS in the two cases and holds promise to appreciate mechanisms having the greatest influence on it. ORIGINALITY/VALUE: The framework can serve as a conceptual guide to facilitate the absorption of innovation in schools and to design future empirical research to better understand the underlying process by which schools strengthen their capacities to become settings conducive to the health of youth.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".