Diversity and inclusion in simulation: addressing ethical and psychological safety concerns when working with simulated participants
Bibliographic record
Abstract
Healthcare learners can gain necessary experience working with diverse and priority communities through human simulation. In this context, simulated participants (SPs) may be recruited for specific roles because of their appearance, lived experience or identity. Although one of the benefits of simulation is providing learners with practice where the risk of causing harm to patients in the clinical setting is reduced, simulation shifts the potential harm from real patients to SPs. Negative effects of tokenism, misrepresentation, stereotyping or microaggressions may be amplified when SPs are recruited for personal characteristics or lived experience. Educators have an ethical obligation to promote diversity and inclusion; however, we are also obliged to mitigate harm to SPs. The goals of simulation (fulfilling learning objectives safely, authentically and effectively) and curricular obligations to address diverse and priority communities can be in tension with one another; valuing educational benefits might cause educators to deprioritise safety concerns. We explore this tension using a framework of diversity practices, ethics and values and simulation standards of best practice. Through the lens of healthcare ethics, we draw on the ways clinical research can provide a model for how ethical concerns can be approached in simulation, and suggest strategies to uphold authenticity and safety while representing diverse and priority communities. Our objective is not to provide a conclusive statement about how values should be weighed relative to each other, but to offer a framework to guide the complex process of weighing potential risks and benefits when working with diverse and priority communities.
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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 | 0.060 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".