Prévenir le VIH et l’hépatite C dans les prisons fédérales canadiennes : l’apport de la recherche et du militantisme dans l’instauration des programmes d’échanges de seringues en prison
Bibliographic record
Abstract
RÉSUMÉ:Les auteurs souhaitent faire le point sur la santé des détenus sous responsabilité fédérale, se centrant plus spécifiquement sur les épidémies de virus de l’immunodéficience humaine (VIH) et d’hépatite C qui s’y propagent. Ils exposeront d’abord le portrait sociodémographique des prisonniers, en focalisant sur les problèmes de toxicomanie qui y prévalent. S’appuyant ensuite sur des études canadiennes et internationales, ils argumenteront pour la mise en place de programmes d’échanges de seringues en prison, démontrant du même coup ses bienfaits pour la société dans son ensemble. Finalement, ils discuteront de l’engagement communautaire des scientifiques et des chercheurs comme une modalité pouvant engendrer leur implantation dans les milieux carcéraux. ABSTRACT:The authors wish to review the health of federal inmates, focusing more specifically on the epidemics of human immunodeficiency virus (HIV) and hepatitis C. They first expose the sociodemographic portrait of prisoners, focusing on substance abuse issues. Then, based on Canadian and international studies, they argue for the introduction of prison needle exchange programs, at the same time demonstrating its benefits to society as a whole. Finally, they will discuss community involvement of scientists and researchers as a modality to lead to the implementation of such programs in prison settings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".