Challenges facing standardised patients representing equity‐deserving groups: Insights from health care educators
Bibliographic record
Abstract
INTRODUCTION: Health professions training programmes increasingly rely on standardised patient (SP) programmes to integrate equity-deserving groups into learning and assessment opportunities. However, little is known about the optimal approach, and many SP programmes struggle to meet these growing needs. This study explored insights from health care educators working with SP programmes to deliver curricular content around equity-deserving groups. METHODS: We interviewed 14 key informants in 2021 who were involved in creating or managing SP-based education. Verbatim transcripts were analysed in an iterative coding process, anchored by qualitative content analysis methodology and informed by two theoretical frameworks: sociologic translation and simulation design. Repeated cycles of data collection and analyses continued until themes could be constructed, aligned with existing theories and grounded in empirical data, with sufficient relevance and robustness to inform educators and curricular leads. RESULTS: Three themes were constructed: (i) creating safety for SPs paid to be vulnerable, (ii) fidelity as an issue broader than who plays the role and (iii) engaging equity-deserving groups. SP work involving traditionally marginalised groups risk re-traumatization, highlighting the importance of (i) informed consent in recruiting SPs, (ii) separating role portrayal from lived experiences, (iii) adequately preparing learners and facilitators, (iv) creating time-outs and escapes for SPs and (v) building opportunity for de-roling with community support. CONCLUSIONS: SP programmes are well positioned to be allies and advocates to equity-deserving groups and to collaborate and share governance of the educational development process from its outset. SP programmes can support the delivery of curricular content around equity-deserving groups by advocating with curricular leadership, building relationships with community partners, facilitating co-creation and co-delivery of educational content and building safety into simulation.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".