Attitudes and Practices of a Sample of Nova Scotian Physicians for the Implementation of HIV Pre-Exposure Prophylaxis
Bibliographic record
Abstract
INTRODUCTION: Pre-exposure prophylaxis (PrEP) is an effective HIV prevention tool that requires the ongoing support of physicians to be accessible. Recently, Nova Scotia experienced a 100% increase in HIV diagnoses. The purpose of this study is to explore the relationship between physicians' support of PrEP, knowledge of PrEP, and PrEP prescribing history using the information-motivation-behavioral (IMB) skills model. METHODS: An online survey was distributed to physicians in Nova Scotia, Canada, and eighty physicians participated. Two exploratory factor analyses were conducted with items from the Support of PrEP scale and Knowledge of PrEP scale. A mediation analysis was conducted to assess if knowledge of PrEP mediated the relationship between support of PrEP and whether physicians have prescribed PrEP in the past. RESULTS: On average, physicians reported strong support for PrEP, and as support for PrEP increased so did knowledge of PrEP. Further, physicians who had prescribed PrEP demonstrated strong knowledge of PrEP and physicians who had not prescribed PrEP reported feeling neutral. The 95% bootstrap confidence interval indirect effect of Support for PrEP on prescription history did not include zero (B = 1.59, 95% BsCI [0.83, 3.57]) demonstrating that the effect of support for PrEP is mediated by knowledge of PrEP. The most commonly identified barrier to prescribing PrEP was the lack of drug coverage among patients. CONCLUSION: The results of the mediation analysis support the IMB skills model regarding support for PrEP, Knowledge of PrEP, and having prescribed PrEP in the past. Our findings suggest that to improve PrEP uptake in Nova Scotia, educational interventions for physicians and universal coverage of the drug would be necessary.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".