Nurse-led PrEP-RN clinic: a prospective cohort study exploring task-Shifting HIV prevention to public health nurses
Bibliographic record
Abstract
OBJECTIVE: To report the results of a nurse-led pre-exposure prophylaxis (PrEP) delivery service. DESIGN: testing. Those significant in bivariate analysis were retained and entered into a binary multiple logistic regression. Hierarchical modelling was used, and only significant factors were retained. SETTING: This study occurred in an urban public health unit and community-based sexually transmitted infection (STI) clinic in Ottawa, Canada. PARTICIPANTS: Of all persons who were diagnosed with a bacterial STI in Ottawa and everyone who presented to our STI clinic during the study period, there were 347 patients who met our high-risk criteria for PrEP; these criteria included patients who newly presented with any of the following: HIV contacts, diagnosed with a bacterial STI or single use of HIV PEP. Further, eligibility could be determined based on clinical judgement. Patients who met the foregoing criteria were appropriate for PrEP-RN, while lower-risk patients were referred to elsewhere. Of the 347 patients who met our high-risk criteria, 47% accepted and 53% declined. Of those who accepted, 80% selected PrEP-registered nurse (RN). PRIMARY AND SECONDARY OUTCOME MEASURES: Uptake, acceptance, engagement and attrition factors of participants who obtained PrEP through PrEP-RN. FINDINGS: 69% of participants who were eligible attended their intake PrEP-RN visit. 66% were retained in care. Half of participants continued PrEP and half were lost to follow-up. We found no significant differences in the uptake, acceptance, engagement and attrition factors of participants who accessed PrEP-RN regarding reason for referral, age, ethnicity, sexual orientation, annual income, education attainted, insurance status, if they have a primary care provider, presence or absence of depression or anxiety and evidence of newly acquired STI during the study period. CONCLUSIONS: Nurse-led PrEP is an appropriate strategy for PrEP 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".