Advancing the case for nurse practitioner‐based models to accelerate scale‐up of HIV pre‐exposure prophylaxis
Bibliographic record
Abstract
AIMS: To explore the factors that position nurse practitioners (NPs) to lead the implementation of HIV pre-exposure prophylaxis. BACKGROUND: The HIV epidemic represents a global health crisis. Reducing new HIV infections is a public health priority, especially for Black and Latino men who have sex with men (MSM). When taken as directed, co-formulated emtricitabine and tenofovir have over 95% efficacy in preventing HIV; however, substantial gaps remain between those who would benefit from pre-exposure prophylaxis (PrEP) and current PrEP prescribing practices. DESIGN: This is a position paper that draws on concurrent assessments of research literature and advanced practice nursing frameworks. METHOD: The arguments in this paper are grounded in the current literature on HIV PrEP implementation and evidence of the added value of nurse-based models in promoting health outcomes. The American Association of Colleges of Nursing's advanced nursing practice competencies were also included as a source of data for identifying and cross-referencing NP assets that align with HIV PrEP care continuum outcomes. CONCLUSIONS: There are four main evidence-based arguments that can be used to advance policy-level and practice-level changes that harness the assets of nurse practitioners in accelerating the scale-up of HIV PrEP. RELEVANCE TO CLINICAL PRACTICE: Global public health goals for HIV prevention cannot be achieved without the broader adoption of PrEP as a prevention practice among healthcare providers. NPs are the best hope for closing this gap in access for the populations that are most vulnerable to HIV infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".