Engaging and supporting standardized patients involved in equity-seeking healthcare training: a qualitative study
Bibliographic record
Abstract
Objectives: This study aims to explore the perspectives of standardized patients previously involved in equity-seeking healthcare training simulation activities to better understand how stakeholders can engage and support standardized patients in the delivery of educational opportunities for healthcare professionals related to equity, diversity, and inclusivity. Methods: A qualitative research study was conducted utilizing semi-structured interviews with persons (N=15) who self-identified as being involved in the development and/or the delivery of simulations related to equity, diversity, and/or inclusion. Participants were recruited via email using internal and public lists for standardized patient programs and through snowball sampling. Interviews were recorded and transcribed verbatim and transcripts were analyzed qualitatively in an iterative coding process, anchored by direct content analysis methodology, and informed by three theoretical perspectives: the standardized patient journey, psychological safety, and empowerment theory. Results: We constructed three main themes: (1) safety should be prioritized throughout the journey; (2) empowerment is key to engagement; and (3) standardized patient trainers are central connectors for safety and empowerment. Conclusions: Through the perspectives of standardized patients, this study has provided insight on strategies to engage and support those participating in equity-seeking simulations. Focused attention on safety and empowerment is warranted, with trainers having a critical role in empowering standardized patients to succeed in equity-seeking simulations. Future research should continue to explore best practices surrounding engaging, supporting, and retaining individuals involved in equity-seeking healthcare training, including comprehensive training for trainers on how to ensure, maintain, and restore standardized patients' psychological safety.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".