Decision Support Tool to Improve Decision-Making for HIV Pre-Exposure Prophylaxis (PrEP): Development Process and Alpha Testing
Bibliographic record
Abstract
BACKGROUND: African, Caribbean, and Black (Black) communities in Canada are disproportionately affected by the HIV epidemic. Pre-exposure prophylaxis (PrEP) is a highly effective option for the prevention of HIV. However, the use of PrEP for HIV prevention among eligible Black clients in Canada remains far below the thresholds necessary to achieve the goal of zero new HIV infections. In a recent study in Toronto, PrEP-eligible Black clients were found to have decisional conflict and unmet decisional needs, which affected the quality of their decision-making process regarding the initiation and adherence to PrEP. There is evidence that decision support tools (DSTs) can improve the quality of a decision, the quality of the decision-making process, the implementation or continuation of the chosen option, and the appropriate use of health services. Despite these benefits, there is currently no DST for PrEP-eligible Black clients being asked to consider PrEP for HIV prevention. OBJECTIVE: Our study aimed to develop a DST to improve PrEP decision-making for Black clients and to evaluate the tool's acceptability and usability. METHODS: We developed and evaluated the PrEP DST for Black patients using the 7-step process outlined in the Ottawa Decision Support Group Guideline for the development and evaluation of DST. To facilitate the implementation of the Ottawa Decision Support Group guideline, we assembled a multidisciplinary team of primary health care providers, researchers, community members with lived experiences, and digital content designers to serve as the steering committee. First, we assessed patients' and primary health care providers' views on decisional support needs, after which we determined the content, design, and distribution plan for the DST. Subsequently, we conducted evidence synthesis, reviews, and appraisal before developing the PrEP DST prototype. The final tool was reviewed by steering committee members for completeness before acceptability and usability testing with potential Black clients and PrEP providers. RESULTS: The web-based DST yielded 27 pages divided into 6 distinct sections. The six sections include (1) an introduction of the DST, (2) clarify your decision, (3) knowledge, (4) a value clarification exercise, (5) support system, and (6) next steps. Both Black clients and PrEP providers reported ease of task performance, general satisfaction, and usefulness of the tool to support decision-making for Black clients. Feedback on usability centered on the need to add a user guide to increase usability. All feedback was incorporated into the final tool. CONCLUSIONS: A PrEP DST for Black clients developed using a systematic process and a multidisciplinary steering committee was acceptable and usable by both Black clients and PrEP providers. Further study (eg, randomized controlled trials) may be needed to evaluate the efficacy of the PrEP DST.
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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 | 0.035 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".