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 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".