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Record W2956148897 · doi:10.1177/1757975919843060

Mieux comprendre les défis de la médicalisation de la prévention du VIH en France : la prophylaxie préexposition au prisme des sciences sociales

2019· article· fr· W2956148897 on OpenAlexaff
Séverine Carillon, Gabriel Girard

Bibliographic record

VenueGlobal Health Promotion · 2019
Typearticle
Languagefr
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceHuman immunodeficiency virus (HIV)PhilosophyMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.415
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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