Decentralizing the delivery of HIV pre-exposure prophylaxis (PrEP) through family physicians and sexual health clinic nurses: a dissemination and implementation study protocol
Bibliographic record
Abstract
BACKGROUND: Gay, bisexual and other men who have sex with men (gbMSM) in Canada continue to experience high rates of incident HIV. Pre-exposure prophylaxis (PrEP, the regular use of anti-HIV medication) reduces HIV acquisition and could reduce incidence. However, there are too few physicians with expertise in HIV care to meet the projected demand for PrEP. To meet demand and achieve greater public health impact, PrEP delivery could be 'decentralized' by incorporating it into front-line prevention services provided by family physicians (FPs) and sexual health clinic nurses. METHODS: This PrEP decentralization project will use two strategies. The first is an innovative knowledge dissemination approach called 'Patient-Initiated CME' (PICME), which aims to empower individuals to connect their family doctors with online, evidence-based, continuing medical education (CME) on PrEP. After learning about the project through community agencies or social/sexual networking applications, gbMSM interested in PrEP will use a uniquely coded card to access an online information module that includes coaching on how to discuss their HIV risk with their FP. They can provide their physician a link to the accredited CME module using the same card. The second strategy involves a pilot implementation program, in which gbMSM who do not have a FP may bring the card to designated sexual health clinics where trained nurses can deliver PrEP under a medical directive. These approaches will be evaluated through quantitative and qualitative methods, including: questionnaires administered to patients and physicians at baseline and at six months; focus groups with patients, FPs, and sexual health clinic staff; and review of sexual health clinic charts. The primary objective is to quantify the uptake of PrEP achieved using each decentralization strategy. Secondary objectives include a) characterizing barriers and facilitators to PrEP uptake for each strategy, b) assessing fidelity to core components of PrEP delivery within each strategy, c) measuring patient-reported outcomes including satisfaction with clinician-patient relationships, and d) conducting a preliminary costing analysis. DISCUSSION: This study will assess the feasibility of a novel strategy for disseminating knowledge about evidence-based clinical interventions, and inform future strategies for scale-up of an underutilized HIV prevention tool.
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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 | 0.128 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".