Cost-effectiveness of peer role play and standardized patients in undergraduate communication training
Bibliographic record
Abstract
BACKGROUND: The few studies directly comparing the methodological approach of peer role play (RP) and standardized patients (SP) for the delivery of communication skills all suggest that both methods are effective. In this study we calculated the costs of both methods (given comparable outcomes) and are the first to generate a differential cost-effectiveness analysis of both methods. METHODS: Medical students in their prefinal year were randomly assigned to one of two groups receiving communication training in Pediatrics either with RP (N = 34) or 19 individually trained SP (N = 35). In an OSCE with standardized patients using the Calgary-Cambridge Referenced Observation Guide both groups achieved comparable high scores (results published). In this study, corresponding costs were assessed as man-hours resulting from hours of work of SP and tutors. A cost-effectiveness analysis was performed. RESULTS: Cost-effectiveness analysis revealed a major advantage for RP as compared to SP (112 vs. 172 man hours; cost effectiveness ratio .74 vs. .45) at comparable performance levels after training with both methods. CONCLUSIONS: While both peer role play and training with standardized patients have their value in medical curricula, RP has a major advantage in terms of cost-effectiveness. This could be taken into account in future decisions.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".