Developing Educational Animations on HIV Pre-exposure Prophylaxis (PrEP) for Women: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Despite experiencing the second-highest rate of HIV incidence in the United States, pre-exposure prophylaxis (PrEP) use remains low among Black women due, in part, to a lack of patients' awareness and providers' knowledge. OBJECTIVE: Our goal was to design animated educational tools informed by patients and women's health providers to address these barriers, specifically for women at risk for HIV. METHODS: Two animation storyboards about PrEP for women were created by academic stakeholders (eg, HIV clinical experts, educators, and HIV peer counselors), one for patients and one for providers. Four focus groups with community members from Baltimore, Maryland and four with women's health providers (eg, obstetrician/gynecologists, midwives, nurse practitioners, and peer counselors) at an academic center were conducted to discuss the storyboards. Transcripts were analyzed using conventional content analysis, and themes were incorporated into the final versions of the animations. RESULTS: Academic stakeholders and 30 focus group participants (n=16 female community members and n=14 women's health providers) described important themes regarding PrEP. The themes most commonly discussed about the patient animation were understandability of side effects, HIV risk factors, messaging, PrEP access, and use confidence. Provider animation themes were indications for PrEP, side effects, and prescribing confidence. CONCLUSIONS: We created two PrEP animations focused on women. Stakeholder feedback highlighted the importance of ensuring the understandability and applicability of PrEP educational materials while including necessary information to facilitate use or prescribing confidence. Both community members and women's health providers reported greater use confidence after viewing the animations.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".