The Role of Standardized Patient Programs in Promoting Equity, Diversity, and Inclusion: A Narrative Review
Bibliographic record
Abstract
PURPOSE: Integrating equity, diversity, and inclusion (EDI) in curricula for training health professionals is a frequent institutional goal. The use of standardized (or simulated) patient programs (SPPs) to support EDI in health sciences training is not well described. Here the authors present a theoretical model based on a synthesis of the literature for using SPPs in EDI training, along with a narrative review of the available literature. METHOD: The authors searched PubMed, Scopus, Science Direct, and Google Scholar databases for studies published between January 2000 and October 2019 describing the use of SPPs to support EDI in health sciences education. Studies were included if they described standardized patient (SP) education involving EDI and reported empiric data about its design, delivery, or effectiveness. The authors conducted a narrative review and provided a synthesis of the available literature, identifying key themes. RESULTS: Out of 117 studies identified, 17 met the inclusion criteria. Most studies (53%; n = 9) focused on cultural competence; many focused on communication with diverse patients (29%; n = 5) or health inequity (18%; n = 3). Studies employed portrayal of diversity (71%; n = 12) or learning objectives supported by diversity (29%; n = 5) as approaches to EDI relevant to SP training. Three primary themes emerged: improving cultural competence, effective communication with diverse patients, and highlighting health inequalities. CONCLUSIONS: This review outlines approaches to EDI-based SPPs, with the perspectives and priorities of institutional approaches in mind. SP education around specific EDI issues is reported; however, programmatic approaches to EDI by SPPs are lacking. More research is needed to provide further evidence for the challenges, effectiveness, and outcomes of developing and implementing EDI-based SPPs in health sciences education.
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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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".