Characterizing Canadian Healthcare Providers’ Behaviour Regarding Prescribing HIV Pre-Exposure Prophylaxis (PrEP) to Women: A Cross-Sectional Survey
Bibliographic record
Abstract
Background: HIV Pre-Exposure Prophylaxis (PrEP) is underused in women. I explored providers’ PrEP prescribing to women using the Capability-Opportunity-Motivation-Behaviour (COM-B) model. Methods: In a survey, Canadian OBGYN, family, internal, and preventive medicine physicians/residents, and pharmacists self-assessed their COM-B from 0-100, following guiding questions between August/2023-March/2024. I descriptively analyzed determinants of PrEP prescribing to cisgender/ transgender women and used multivariable regression modeling to identify construct correlates. Results: Nearly a quarter of providers had prescribed to cis and trans women (24.8%, CI=0.19-0.3; and 23.8%, CI=0.19-0.29). Providers had high prescribing Motivation (median=80, IQR=61, 91.3 for cis women; median=81, IQR=63, 100 for trans women), and modest Capability (median=54, IQR=25, 80; median=58, IQR=25, 84.3) and Opportunity (median=41, IQR=16, 70; median=40, IQR = 10, 73.5). In the adjusted models, Capability (AOR=1.34, p=0.002; AOR=1.45, p=0.017) and Opportunity (AOR=1.19, p<0.001; AOR=1.22, p=0.009) were significantly associated with prescribing to cis and trans women, while Motivation was not. Conclusions: Prescribing had occurred only in a quarter of providers, likely due to low Capability and Opportunity. Future interventions should address these constructs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".