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Record W2055402237 · doi:10.1371/journal.pone.0105283

Preparing for PrEP: Perceptions and Readiness of Canadian Physicians for the Implementation of HIV Pre-Exposure Prophylaxis

2014· article· en· W2055402237 on OpenAlexafffundabout
Malika Sharma, James Wilton, Heather Senn, Shawn Fowler, Darrell H. S. Tan

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSt. Michael's HospitalHassle Free ClinicCanadian AIDS Treatment Information ExchangeUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPre-exposure prophylaxisFamily medicineMedicineOdds ratioHuman immunodeficiency virus (HIV)Public healthConfidence intervalMen who have sex with menNursingInternal medicine

Abstract

fetched live from OpenAlex

Recent evidence has demonstrated the efficacy of pre-exposure prophylaxis (PrEP) for HIV prevention, but concerns persist around its use. Little is known about Canadian physicians' knowledge of and willingness to prescribe PrEP. We disseminated an online survey to Canadian family, infectious disease, internal medicine, and public health physicians between September 2012-June 2013 to determine willingness to prescribe PrEP. Criteria for analysis were met by 86 surveys. 45.9% of participants felt "very familiar" with PrEP, 49.4% felt that PrEP should be approved by Health Canada, and 45.4% of respondents were willing to prescribe PrEP. Self-identifying as an HIV expert (odds ratio, OR = 4.1, 95% confidence interval, CI = 1.6-10.2), familiarity with PrEP (OR = 5.0, 95%CI = 1.3-19.0) and having been asked by patients about PrEP (OR = 4.0, 95%CI = 1.5-10.5) were positively associated with willingness to prescribe PrEP on univariable analysis. The latter two were the strongest predictors on multivariate analysis. Participants cited cost and efficacy as major concerns. 75.3% did not feel that information had been adequately disseminated among physicians. In summary, Canadian physicians demonstrate varying levels of support for PrEP and express concerns about its implementation. Further research on real-world effectiveness, continuing medical education, and clinical support is needed to prepare physicians for this prevention strategy.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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 designBench or experimental
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

Citations74
Published2014
Admission routes3
Has abstractyes

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