A Multicomponent Strategy to Improve HIV Pre-Exposure Prophylaxis in a Southern US Jail: Protocol for a Type 3 Hybrid Implementation-Effectiveness Trial
Bibliographic record
Abstract
BACKGROUND: Pre-exposure prophylaxis (PrEP) is an effective approach for preventing HIV infection, but it is underutilized by populations who may benefit the most, including people living in the Southern United States and those involved in the criminal legal (CL) system. Improving the access and use of PrEP for these groups could decrease HIV-related health disparities. Beyond individual outcomes, HIV prevention for CL-involved people can have a significant public health impact on HIV incidence due to a high turnover between jails and the community. OBJECTIVE: We will develop, implement, and evaluate a multicomponent PrEP implementation strategy for the Dallas County Jail (DCJ) to increase the initiation of this HIV-preventive intervention for CL-involved individuals. METHODS: This is a type 3 hybrid implementation-effectiveness study that takes a combined approach by assessing the implementation of a strategy to identify candidates for PrEP at the DCJ and linking them to PrEP providers upon community re-entry while also gathering information about clinical outcomes. The approach is guided by the EPIS (exploration, preparation, implementation, sustainment) framework. Initial formative work (exploration) involves qualitative interviews of diverse key stakeholders to identify factors that may influence linkage to PrEP after jail release. These findings will undergo rapid qualitative analysis (preparation) to inform the adaptation of a multicomponent jail PrEP implementation strategy protocol. This approach, which will include an electronic health record (EHR) prediction model and integration of a PrEP patient navigator into the jail health team, will allow medical providers and the navigator at the DCJ to engage individuals most likely to benefit in shared decision-making about PrEP and navigate them to community PrEP care (implementation) in a process that begins before release from jail and ends with successful care linkage. Regular quantitative and qualitative evaluations of this approach will allow for ongoing stakeholder input, refinement of the implementation strategy, and maintenance of the program (sustainment). RESULTS: Findings from 26 qualitative interviews (9 formerly incarcerated individuals, 9 county jail staff, and 8 employees of community organizations) have been obtained, analyzed, and mapped to an implementation strategy formalized in a jail PrEP protocol. An HIV risk prediction model based on EHR data to identify individuals most likely to benefit from PrEP has been developed and internally validated and is ready to be deployed. We anticipate the availability of preliminary study findings in 2026. CONCLUSIONS: This study will provide key insights into the feasibility and effectiveness of a PrEP implementation strategy among people at increased risk of HIV acquisition in an urban jail in Southern United States. This practical and scalable strategy can be used as a model for other urban jails to address HIV-related inequities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64813.
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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 | 0.058 | 0.067 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.123 | 0.018 |
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".