A new section for <i>Global Health Promotion</i> journal: doing research in health promotion
Bibliographic record
Abstract
The question of the body of knowledge to use to base health promotion policies and practices has been raised from the outset. Over time and with the vast increase in the scientific knowledge base available, this question has taken a radically different turn. Indeed, while it has long been about relying on scarce and disparate research findings, it is no longer the case today. Long described as a multidisciplinary enterprise (1) and eclectic from methods’ point of view (2), health promotion research is gradually constituting itself as a distinct field, a space in which researchers share an identity, frameworks of thinking and of knowledge production, and an explicit ethical framework (3).It is with the goal of formalizing the constitution of a distinct field for health promotion research and of mobilizing the researchers who participate in it that we have launched the publication project of a Global Handbook of Health Promotion Research (4). In order to concretely base our structuring of the field on the practices implemented in projects led by researchers who identify themselves in this field, we have widely circulated a call for contributions. This call was a success that greatly exceeded our expectations. Having received more than 80 chapters from all continents, we have opted to develop and publish a three-volume handbook.
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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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.031 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.054 | 0.055 |
| Insufficient payload (model declined to judge) | 0.052 | 0.034 |
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".