Diversity and inclusion in simulation: addressing ethical and psychological safety concerns when working with simulated participants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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 it