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Record W2901783500 · doi:10.1111/1467-9566.12826

Is <scp>HIV</scp> prevention creating new biosocialities among gay men? Treatment as prevention and pre‐exposure prophylaxis in Canada

2018· article· en· W2901783500 on OpenAlexaffabout
Gabriel Girard, San Patten, Marc‐André LeBlanc, Barry D. Adam, Edward A. Jackson

Bibliographic record

VenueSociology of Health & Illness · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of WindsorCanadian AIDS Treatment Information ExchangeUniversité de Montréal
Fundersnot available
KeywordsPre-exposure prophylaxisContext (archaeology)Human immunodeficiency virus (HIV)Diversity (politics)Gender studiesMen who have sex with menHomosexualityExperiential learningSociologyMedicineFamily medicinePedagogyHistory

Abstract

fetched live from OpenAlex

The advancements of "treatment as prevention" (TasP), "undetectable viral load" (UVL) and "pre-exposure prophylaxis" (PrEP) are redefining HIV prevention standards. Relying on the concept of biosociality, this article explores how gay men rally around, debate, and sometimes disagree about these emerging HIV prevention technologies. This article is based on data from the Resonance Project, a Canadian community-based research project. Twelve focus groups (totalling 86 gay and bisexual men) were held in three Canadian cities (Montreal, Toronto, Vancouver) in 2013-2014. Respondents view UVL and PrEP through the prism of their generational experience of HIV prevention. In this respect, biosocialities highlight an experiential dimension that is tied to the context of the HIV epidemic. The biosocialities of HIV prevention are also built around serological identities. However, our study shows the diversity of these positions. Analysis grounded in biosocialities is useful for better understanding how scientific information circulates, is made sense of, and generates debate among gay men.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.368
Teacher spread0.335 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations35
Published2018
Admission routes2
Has abstractyes

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