Mieux comprendre les défis de la médicalisation de la prévention du VIH en France : la prophylaxie préexposition au prisme des sciences sociales
Bibliographic record
Abstract
Résumé : Le recours aux traitements antirétroviraux pour la prévention du VIH transforme en profondeur le contexte des interventions dans ce domaine. La prophylaxie pré-exposition (Prep) en constitue l'une des facettes les plus visibles. Pour autant, l'utilisation de la Prep en France s'avère limitée. L'outil peine à trouver son public parmi les populations ciblées. Comment expliquer la sous-utilisation d'une approche de prévention dont la haute efficacité est pourtant démontrée ? Les réponses à cette question gagneraient à s'enrichir des sciences humaines et sociales tant pour penser les conditions de l'appropriation de l'outil par les publics ciblés que pour identifier les impensés et les logiques qui sous-tendent son déploiement. Loin de se limiter au VIH, la réflexion critique sur le recours aux médicaments comme outils de prévention ouvre des questions pertinentes pour le champ de la promotion de la santé.
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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 | 0.045 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| 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".