Decision conflict and the decision support needs of HIV PrEP-eligible Black patients in Toronto regarding the adoption of PrEP for HIV prevention
Bibliographic record
Abstract
Objectives: This study examined factors contributing to decision conflict and the decision support needs of PrEP-eligible Black patients. Methods:The Ottawa Decision Support Framework (ODSF) was used to guide the development of a key informant guide used for qualitative data collection. Black patients assessed by healthcare providers as meeting the basic criteria for starting PrEP were recruited through the St. Michael's Hospital Academic Family Health Team and clinical and community agencies in Toronto. Participants were interviewed by trained research staff. Qualitative content analysis was guided by the ODSF, and analysis was done using the Nvivo. Results: Four women and twenty-five men (both heterosexual and men who have sex with men) were interviewed. Participants reported having difficulty in decision making regarding adoption of PrEP. The main reasons for decision-conflict regading PrEP adoption were: lack of adequate information about PrEP, concerns about the side effects of PrEP, inability to ascertain the benefits or risk of taking PrEP, provider's lack of adequate time for interaction during clinical consultation, and perceived pressure from healthcare provider. Participants identified detailed information about PrEP, and being able to clarify how their personal values align with the benefits and drawbacks of PrEP as their decision support needs. Conclusion:Many PrEP-eligible Black patients who are prescribed PrEP have decision conflict which often causes delay in decision making and sometimes rejection of PrEP. Healthcare providers should offer decision support to Black patients who are being asked to consider PrEP for HIV prevention.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".