Development of a Clinic-Based, Sociostructural Intervention to Improve the Provision of Pre-Exposure Prophylaxis for Cisgender Women: Formative Study Using the Assessment, Decision, Adaptation, Production, Topical Experts, Integration, Training, and Testing (ADAPT-ITT) Framework
Bibliographic record
Abstract
BACKGROUND: Cisgender women (subsequently referred to as women) account for 23% of new HIV diagnoses in the United States. There are significant sociostructural barriers to engagement and retention in the pre-exposure prophylaxis (PrEP) cascade, particularly for Black women. OBJECTIVE: In response to the lack of evidence-based interventions (EBIs) to improve PrEP initiation, adherence, and persistence among women in the United States, we developed a clinic-based, sociostructural intervention focused on engagement and retention in the PrEP cascade for women. METHODS: We used the Assessment, Decision, Adaptation, Production, Topical experts, Integration, Training, and Testing (ADAPT-ITT) model to adapt two Centers for Disease Control and Prevention (CDC) best practices in HIV prevention: HIV PrEP Services for Urban Women and Project Shikamana to create a culturally appropriate EBI responsive to Black women's HIV prevention needs. In this paper, we focus on the first 6 steps of iterative adaptation in preparation for pilot testing. We conducted semistructured interviews and focus group discussions with key populations to inform and guide intervention development and used theater testing to evaluate the mock-up of the prototype. We conducted rapid qualitative analysis to identify key themes related to delivering PrEP to the intended population and engaged subject matter experts to refine the prototype. RESULTS: For the Assessment phase, we conducted 10 in-depth interviews with key informants from community-based and HIV-prevention organizations and led 7 focus group discussions (n=4-8) to guide intervention development among health care providers (n=2 groups), PrEP navigators and educators (n=1 group), and female patients (n=4 groups). Key themes included population-specific barriers to PrEP use, namely accessibility and availability, perceived risk, and stigma. In addition, participants advised on model adaptation specific to PrEP navigation, clinic-level training, and social support. For the Decision phase, we selected 2 EBIs from the CDC HIV Compendium of Best Practices. For the Adaptation phase, we adapted and theater tested a preliminary intervention for feedback. For the Production phase, using feedback from theater testing, we created a prototype of the Women's PrEP Project (W-PrEP) designed to address patient-, provider-, and clinic-level barriers to the provision of PrEP for women through a clinic-wide intervention delivering education, resources, and support (including PrEP navigation). For the Topical experts and Integration phases, we collected iterative feedback from our advisory board and subject matter experts and integrated feedback into the final prototype. CONCLUSIONS: The adaptation of the W-PrEP integrated key elements of the local context for women with potential exposure to HIV, as well as health care providers, clinic staff, and PrEP navigators. Next steps include training clinic staff in a real-world setting to pilot-test the acceptability, feasibility, and preliminary effectiveness of the intervention.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".