The role of people living with HIV as patient instructors – reducing stigma and improving interest around HIV care among medical students
Bibliographic record
Abstract
People living with HIV/AIDS (PHAs) are increasingly recognized as experts in HIV and their own health. We developed a simulated clinical encounter (SCE) in which medical students provided HIV pre- and post-test counselling and point-of-care HIV testing for PHAs as patient instructors (PHA-PIs) under clinical preceptor supervision. The study assessed the acceptability of this teaching tool with a focus on assessing impact on HIV-related stigma among medical students. University of Toronto pre-clerkship medical students participated in a series of SCEs facilitated by 16 PHA-PIs and 22 clinical preceptors. Pre- and post-SCE students completed the validated Health Care Provider HIV/AIDS Stigma Scale (HPASS). HPASS measures overall stigma, as well as three domains within HIV stigma: stereotyping, discrimination, and prejudice. Higher scores represented higher levels of stigma. An additional questionnaire measured comfort in providing HIV-related care. Mean scores and results of paired t-tests are presented. Post-SCE, students (n = 62) demonstrated decreased overall stigma (68.74 vs. 61.81, p < .001) as well as decreased stigma within each domain. Post-SCE, students (n = 67) reported increased comfort in providing HIV-related care (10.24 vs. 18.06, p < .001). Involving PHA-PIs reduced HIV-related stigma among medical students and increased comfort in providing HIV-related care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".