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Record W3098109213 · doi:10.1097/jnc.0000000000000219

Issues Associated With Prescribing HIV Pre-exposure Prophylaxis for HIV Anxiety: A Qualitative Analysis of Australian Providers' Views

2020· article· en· W3098109213 on OpenAlexfundno aff
Anthony K J Smith, Martin Holt, Bridget Haire, Christy E. Newman

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersUniversity of New South WalesAustralian GovernmentMcGill University
KeywordsSerodiscordantAnxietyPre-exposure prophylaxisHuman immunodeficiency virus (HIV)MedicineQualitative researchFamily medicineMen who have sex with menPsychiatryViral loadAntiretroviral therapy

Abstract

fetched live from OpenAlex

ABSTRACT: HIV pre-exposure prophylaxis (PrEP) can alleviate anxiety about acquiring HIV, particularly for gay men and other men who have sex with men. However, research with PrEP providers has rarely examined HIV anxiety. We conducted 25 semistructured interviews in 2019-2020 with PrEP providers in New South Wales and Western Australia, and analyzed data thematically. Participants included general practitioners and sexual health nurses and doctors. Our analysis explores providers' views on providing PrEP to reduce HIV anxiety for gay men, serodiscordant couples where the partner with HIV has an undetectable viral load, and for "worried well" individuals who the providers speculated might have undisclosed risk. Although providers viewed PrEP as beneficial for many people's personal lives and relationships, they felt cautious about prescribing PrEP solely for HIV anxiety, while at the same time reporting that they prescribed PrEP if individuals insisted on it and had no medical contraindications.

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)0.008
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 score0.264
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.040
GPT teacher head0.391
Teacher spread0.352 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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