Linking Black women to PrEP care using warm handoff referrals from emergency departments to local PrEP clinics
Bibliographic record
Abstract
New Human Immunodeficiency Virus (HIV) cases continue to disproportionately burden cisgender Black/African American women in the United States due to a confluence of structural and systemic factors. Pre-exposure prophylaxis (PrEP) is a safe and effective HIV prevention option, yet there is a striking gap between PrEP eligibility and uptake among cisgender Black women. The current study evaluates a novel warm handoff process in a hospital emergency department setting linking eligible women to local PrEP clinics within 72 hours of hospital discharge in a large southwestern metropolitan city. Participants received follow-up telephone consultations at 1-month (T1), 3-months (T2), and 6-months (T3) post-enrollment. Of 40 participants, one was successfully linked to their initial PrEP clinic visit. One additional participant reported attending their PrEP visit, but staff were unable to confirm linkage. Twenty-eight percent of participants attended follow-up telephone visits at T1, T2, and T3, while 35% of participants attended a combination of some, and 37% of participants did not engage in any follow-up visits. Findings suggest that culturally tailored linkage interventions are suitable mechanisms for engaging cisgender Black women in PrEP care. Implications for future research include exploring the sustainability and scalability of such interventions are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".