Trois défis pour l'évaluation en promotion de la santé
Bibliographic record
Abstract
L'évaluation des interventions en promotion de la santé est de plus en plus répandue, répondant ainsi à deux besoins cruciaux dans le domaine, soit: 1) justifier les ressources investies en démontrant ses effets et 2) soutenir les processus innovants nécessaires pour aligner les interventions avec les principes, valeurs et engagements de la Charte d'Ottawa pour la promotion de la santé, document fondateur du champ. Dans cet article, nous proposons que dans un cas comme dans l'autre, cet alignement que nous défendons comme inhérent à la promotion de la santé, pose des problèmes cruciaux pour l'évaluation. Ces problèmes se traduisent en trois défis de pertinence auxquels l'évaluateur en promotion de la santé doit confronter sa pratique: 1) définir l'intervention à évaluer de façon à élaborer des questions d'évaluation pertinentes; 2) mettre en œuvre des méthodologies de recherche adéquates et rigoureuses; et 3) produire des connaissances pertinentes pour l'action. (Promot Educ 2008; Supp(1): 17-21)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.431 | 0.525 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| 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".