Standardized Patients or Conventional Lecture for Teaching Communication Skills to Undergraduate Medical Students: A Randomized Controlled Study
Bibliographic record
Abstract
OBJECTIVE: The conduct of a medical interview is a challenging skill, even for the most qualified physicians. Since a training is needed to acquire the necessary skills to conduct an interview with a patient, we compared role-play with standardized patients (SP) training and a conventional lecture for the acquisition of communications skills in undergraduate medical students. METHODS: An entire promotion of third year undergraduate medical students, who never received any lessons about communications skills, were randomized into 4 arms: 1) SP 2 months before the testing of medical communications skills (SP); 2) conventional lecture 2 months before the testing (CL); 3) two control groups (CG) without any intervention, tested either at the beginning of the study or two months later. Students were blindly assessed by trained physicians with a modified 17-items Calgary-Cambridge scale. RESULTS: 388 students (98.7%) participated. SP performed better than CL, with significant statistical differences regarding 5 skills: the use of open and closed questions, encouraging patient responses, inviting the patient to clarify the missing items, encouraging of the patient's emotions, and managing the time and the conduct of the interview. The SP group specifically improved communications skills between the SP training and testing sessions regarding 2 skills: the use of open and closed questions and encouraging patient responses. No improvements in communications skills were observed in CG between the two time points, ruling out a possible time effect. CONCLUSION: Role-play with standardized patients appears more efficient than conventional lecture to acquire communication skills in undergraduate medical students.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".