Mapping Implementation Strategies to Address Barriers to Pre-Exposure Prophylaxis Use Among Women Through POWER Up (Pre-Exposure Prophylaxis Optimization Among Women to Enhance Retention and Uptake): Content Analysis
Bibliographic record
Abstract
BACKGROUND: Black cisgender women (hereafter referred to as "women") experience one of the highest incidences of HIV among all populations in the United States. Pre-exposure prophylaxis (PrEP) is an effective biomedical HIV prevention option, but uptake among women is low. Despite tailored strategies for certain populations, including men who have sex with men and transgender women, Black women are frequently overlooked in HIV prevention efforts. Strategies to increase PrEP awareness and use among Black women are needed at multiple levels (ie, community, system or clinic, provider, and individual or patient). OBJECTIVE: This study aimed to identify barriers and facilitators to PrEP uptake and persistence among Black cisgender women and to map implementation strategies to identified barriers using the CFIR (Consolidated Framework for Implementation Research)-ERIC (Expert Recommendations for Implementing Change) Implementation Strategy Matching Tool. METHODS: We conducted a secondary analysis of previous qualitative studies completed by a multidisciplinary team of HIV physicians, implementation scientists, and epidemiologists. Studies involved focus groups and interviews with medical providers and women at a federally qualified health center in Chicago, Illinois. Implementation science frameworks such as the CFIR were used to investigate determinants of PrEP use among Black women. In this secondary analysis, data from 45 total transcripts were analyzed. We identified barriers and facilitators to PrEP uptake and persistence among cisgender women across each CFIR domain. The CFIR-ERIC Implementation Strategy Matching Tool was used to map appropriate implementation strategies to address barriers and increase PrEP uptake among Black women. RESULTS: Barriers to PrEP uptake were identified across the CFIR domains. Barriers included being unaware that PrEP was available (characteristics of individuals), worrying about side effects and impacts on fertility and pregnancy (intervention characteristics), and being unsure about how to pay for PrEP (outer setting). Providers identified lack of training (characteristics of individuals), need for additional clinical support for PrEP protocols (inner setting), and need for practicing discussions about PrEP with women (intervention characteristics). ERIC mapping resulted in 5 distinct implementation strategies to address barriers and improve PrEP uptake: patient education, provider training, PrEP navigation, clinical champions, and electronic medical record optimization. CONCLUSIONS: Evidence-based implementation strategies that address individual, provider, and clinic factors are needed to engage women in the PrEP care continuum. Tailoring implementation strategies to address identified barriers increases the probability of successfully improving PrEP uptake. Our results provide an overview of a comprehensive, multilevel implementation strategy (ie, "POWER Up") to improve PrEP uptake among women. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1371/journal.pone.0285858.
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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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".