Standardized Patient Education Focused on Equity Deserving Groups
Bibliographic record
Abstract
INTRODUCTION: Health professions training programs must train future healthcare providers to meet the needs of equity-deserving patient populations. Standardized patient (SP) programs are one mechanism by which this training can occur. Our aim was to develop a set of recommendations for SP programs and educators around planning, organizing, and delivering SP-based education involving equity-deserving groups. METHODS: We undertook a qualitative analysis of interview transcripts of SPs, educators, and trainers involved in SP work with equity-deserving groups. Subsequently, we conducted a three-stage modified Delphi process to generate recommendations. RESULTS: We derived 10 tips to help stakeholders improve SP-based education involving equity-deserving groups. The underlying themes included collaborative involvement, including co-creation and co-delivery of content with members of equity-deserving groups, as well as consistent prioritization of the needs of SPs throughout the process. CONCLUSIONS: Our findings suggest ways in which SP programs and educators can better train future healthcare providers to meet the needs of equity-deserving patient populations.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".