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Record W2265573664 · doi:10.1177/0956462415589896

Preparing for pre-exposure prophylaxis: perceptions and readiness of Canadian pharmacists for the implementation of HIV pre-exposure prophylaxis

2015· article· en· W2265573664 on OpenAlexaffabout
Deborah Yoong, Mark Naccarato, Malika Sharma, James Wilton, Heather Senn, Darrell H. S. Tan

Bibliographic record

VenueInternational Journal of STD & AIDS · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsHassle Free ClinicThe Wilson CentreUniversity Health NetworkUniversity of TorontoCanadian AIDS Treatment Information ExchangeSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePre-exposure prophylaxisHuman immunodeficiency virus (HIV)Family medicineIntensive care medicineMen who have sex with menSyphilis

Abstract

fetched live from OpenAlex

Pre-exposure prophylaxis (PrEP) has been shown to reduce the risk of HIV transmission but has the potential to cause harm if not used properly. Pharmacists are well-positioned to foster PrEP's efficacy but little is known whether they would endorse it as an HIV prevention tool. The objective of the study was to determine Canadian HIV pharmacists' support for PrEP and to identify current barriers to promoting PrEP. Canadian pharmacists with experience in HIV care were invited to complete an online survey about their experiences, opinions, and learning needs regarding PrEP from December 2012 to January 2013. Among the 59 surveys received, 48 met criteria for final analysis. Overall, 33 (69%) respondents would provide education positively supporting the use of PrEP and 26 (54%) believed Health Canada should approve PrEP for use in Canada. Familiarity with the concept of PrEP and practice characteristics examined did not appear to be significantly associated with support for PrEP in univariable analyses. The principal barriers to promoting PrEP included inadequate drug coverage and insufficient knowledge to educate others. Many Canadian HIV pharmacists would endorse PrEP for high-risk patients; however, wider dissemination of information and lower drug costs may be needed to make PrEP more widely promoted.

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)0.001
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 score0.422
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.034
GPT teacher head0.396
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.

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

Citations23
Published2015
Admission routes2
Has abstractyes

Explore more

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