Utilization of an Animated Electronic Health Video to Increase Knowledge of Post- and Pre-Exposure Prophylaxis for HIV Among African American Women: Nationwide Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Despite renewed focus on biomedical prevention strategies since the publication of several clinical trials highlighting the efficacy of pre-exposure prophylaxis (PrEP), knowledge of postexposure prophylaxis (PEP) and PrEP continues to remain scarce among women, especially among African American women who are disproportionally affected by HIV. In an effort to address this barrier and encourage uptake of PEP and PrEP, an electronic health (eHealth) video was created using an entertainment-education format. OBJECTIVE: The study aimed to explore the feasibility, acceptability, and preference of an avatar-led, eHealth video, PEP and PrEP for Women, to increase awareness and knowledge of PEP and PrEP for HIV in a sample of African American women. METHODS: A cross-sectional, Web-based study was conducted with 116 African American women aged 18 to 61 years to measure participants' perceived acceptability of the video on a 5-point scale: poor, fair, good, very good, and excellent. Backward stepwise regression was used to the find the outcome variable of a higher rating of the PEP and PrEP for Women video. Thematic analysis was conducted to explore the reasons for recommending the video to others after watching the eHealth video. RESULTS: Overall, 89% of the participants rated the video as good or higher. A higher rating of the educational video was significantly predicted by: no current use of drugs/alcohol (beta=-.814; P=.004), not having unprotected sex in the last 3 months (beta=-.488; P=.03), higher income (beta=.149; P=.03), lower level of education (beta=-.267; P=.005), and lower exposure to sexual assault since the age of 18 years (beta=-.313; P=.004). After watching the eHealth video, reasons for recommending the video included the video being educational, entertaining, and suitable for women. CONCLUSIONS: Utilization of an avatar-led eHealth video fostered education about PEP and PrEP among African American women who have experienced insufficient outreach for biomedical HIV strategies. This approach can be leveraged to increase awareness and usage among African American women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".