Assessing the Potential for Nurse-Led HIV Pre- and Postexposure Prophylaxis in Ontario
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: HIV prevention efforts in Ontario require increased implementation of strategies including post- and pre-exposure prophylaxis. Access to these interventions could be improved by their provision through nurse-led models of care. We assessed nurses' readiness to deliver these interventions using a behavioral change framework. METHODS: We distributed an online survey to nurses in every Ontario sexual health clinic, HIV clinic, and community health center between March-June 2018, to determine the level of support for nurse-led postexposure prophylaxis/pre-exposure prophylaxis; we also explored nurses' "capabilities," "opportunities," and "motivations" for providing postexposure prophylaxis/pre-exposure prophylaxis. RESULTS: Overall, 72.7% of respondents supported implementation of both nurse-led postexposure prophylaxis and pre-exposure prophylaxis. More experienced nurses were less likely to support nurse-led postexposure prophylaxis and pre-exposure prophylaxis (adjusted odds ratio = 0.55 per decade nursing, 95% confidence interval (0.37, 0.82)). Nurses reported a high degree of knowledge of topics related to postexposure prophylaxis/pre-exposure prophylaxis, with the exception of creatinine interpretation. CONCLUSIONS: Ontario nurses report high levels of support for nurse-led postexposure prophylaxis and pre-exposure prophylaxis and are well positioned to provide these interventions. Targeted education and implementation efforts are needed to engage these nurses in postexposure prophylaxis and pre-exposure prophylaxis delivery.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".