Bibliographic record
Abstract
La qualité de la mise en œuvre d’une intervention de promotion de la santé est essentielle pour garantir son adaptation au contexte et aux besoins des populations. Pourtant, elle est encore négligée dans les recherches. Alors qu’elle est entreprise depuis les années 1930 dans l’étude des politiques publiques, le domaine de la santé fait face à une prolifération de termes qui ne facilite pas son enseignement et son organisation. Ce commentaire montre qu’il n’existe pas de différence notoire entre la recherche sur la mise en œuvre ( implementation science ) et la recherche de mise en œuvre ( implementation research ). Plutôt qu’un débat sémantique, il est important de rendre cette analyse plus systématique avec des démarches scientifiques et interdisciplinaires. La recherche concernant la mise en œuvre d’une intervention de promotion de la santé doit chercher à comprendre comment rendre nos actions plus équitables et plus efficaces.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".