Evidence-based health promotion: an emerging field
Bibliographic record
Abstract
There is much debate around the use of evidence in health promotion practice. This article aims to sharpen our understanding of this matter by reviewing and analyzing the 26 case studies presented in this special issue. These case studies suggest that health promotion practitioners are using a wide range of research evidence in interventions for high-risk individuals, entire populations, and vulnerable groups according to all five strategies for action described in the Ottawa Charter for Health Promotion. In nearly every case, practitioners had to mediate and adapt research evidence for their case. Eight key levers helped practitioners embed research evidence into practice: local and cultural relevance of the evidence, community capacity-building, sustained dialogue from the outset with all stakeholders, established academic-supported partnerships, communication that responds to organizational and political readiness, acknowledgement and awareness of gaps between evidence and practice, advocacy, and adequate earmarked resources. These case studies provide some evidence that there is an evidence-based health promotion, that this evidence base is broad, and that practitioners use different strategies to adapt it for their case.
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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 | 0.274 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.041 | 0.043 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.025 | 0.026 |
| 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".