Involving Patients Throughout Sensitive Simulation
Bibliographic record
Abstract
BACKGROUND: While people with lived experience (PLE) are increasingly included in health professions education, their role in simulation often remains limited to narrative sharing. This initiative explored how involving PLE throughout the design and delivery of a pharmacy simulation on PrEP (a preventive HIV treatment) and sexual and gender diversity shaped students' learning about inclusive, person-centred communication in consultations involving stigma and marginalisation. APPROACH: A PrEP-related simulation involving a non-binary patient was integrated into a third-year pharmacy course. Sessions took place in a fully equipped lab replicating a community pharmacy, with 10 stations and professional actors as standardised patients. Six PLE informed learning objectives, scenario design and debriefing. One PLE trained the actors, observed simulations and participated in debriefings. Their involvement supported inclusive, person-centred communication around sexuality, gender diversity and HIV prevention. EVALUATION: A mixed-methods design assessed impact. Survey responses (n = 109) indicated increased confidence in PrEP-related care and strong appreciation for PLE's presence. Most students had little personal and professional exposure to PrEP, and 93% agreed the PLE enriched their learning. Interviews (n = 14) highlighted how PLE involvement helped demystify stigmatised topics, fostered self-reflection and emphasised the value of lived experience. Students noted enhanced realism and psychological safety. IMPLICATIONS: Involving PLE throughout simulation-from codesign to debriefing-helped reduce stigma, promote inclusive communication and enhance scenario authenticity. Students felt better prepared for consultations involving PrEP or sexual and gender diversity. This approach may help amplify the patient's voice in health education and could be adapted to other sensitive or stigmatised health topics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".