Development of a Web-Based HIV Pre-Exposure Prophylaxis (PrEP) Decision Support Tool to Improve Decision-Making for PrEP-eligible Black Patients (Preprint)
Bibliographic record
Abstract
BACKGROUND Black communities in Canada are disproportionately affected by the HIV epidemic. There is currently no patient decision support intervention for Black patients being asked to consider PrEP for HIV prevention, especially in the context of previous studies indicating that most of these patients do have decisional conflict regarding PrEP. OBJECTIVE The aim of this project was to develop a decision support tool to improve decision-making for PrEP-eligible Black clients. METHODS Using the Ottawa Decision Aids Development and Evaluation Guideline, a multidisciplinary team steered the development and evaluation of the DST in a seven steps process: Assess needs; Assess feasibility; Define the objectives of the aids; Identify the framework of decision support; Select the methods of decision support to be used in the aid; Select the designs and measures to evaluate the aid; and Plan dissemination. Both potential PrEP clients and providers reviewed the DST for usability and provided feedback. RESULTS The development process resulted in a web-based DST with 6 sections: Introductory section; Clarify your decision section, Information about the benefits and drawbacks of various prevention methods section; Value clarification exercise section; Identifying support system section; Next steps section. Both potential PrEP clients and PrEP providers expressed satisfaction with the use of the DST. CONCLUSIONS A decision support tool was developed for PrEP-eligible Black patients to enhance their decision-making process for HIV prevention options. Potential users (Black patients and clinicians) found it usable.
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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".