Du pilotage à la gouvernance, prendre en compte la qualité d'objets complexes : pour une méthodologie d'évaluation des actions d'éducation à la santé
Bibliographic record
Abstract
Health education is a recent field, its actions defined as complex objects need to be evaluated. The evaluation has to be guided by precise political goals, simplified by “running”, “regulation”, and “governance” approaches, since these approaches mean different paradigms and epistemologies that need to be clarified. Works about evaluation methodology already exist in other fields (education, health, etc.): their updated review allow transpositions, knowing that evaluation crosses all fields, and doesn't belong to any of them. Considering the literature review, a first concept appears: evaluation is the fact of assessing quality or non quality of the studied objects. Then, it gives first clues to design evaluation of health education actions, guided by what is considered as quality for these actions. Quality definitions vary with paradigms and epistemologies underlined by predefined political goals. If complexity is clearly précised, we can apply the three principles of Edgar Morin: dialogic, recursive, and hologrammatic. These principles allow to exit from the linear logic or of binary opposition, not convenient for complex objects. By clarifying scientific concepts, a methodology of health education evaluation may be brought out, strengthened by concrete examples.
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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 | 0.145 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".