The effectiveness of unannounced standardised patients in the clinical setting as a teaching intervention
Bibliographic record
Abstract
PURPOSE: Teaching medical students to spontaneously identify biopsychosocial issues (e.g. family violence) remains a challenge. We examined the extent to which using unannounced standardised patients (SPs) presenting in a clerk's clinical setting could assist with this teaching challenge. METHODS: All clerks attended a family violence seminar in their family medicine rotation. Intervention students additionally saw an unannounced SP portraying 1 of 2 scenarios in their preceptor's office during the rotation, and received immediate feedback about their performance. An end of rotation objective structured clinical examination (OSCE) included an SP presentation similar to that seen by the intervention students. RESULTS: Clerks who received the intervention demonstrated increased questioning about family violence, from 0% (0 of 29 students) to 19% (5 of 26 students) in 1 OSCE scenario (P = 0.019), and from 40% (12 of 30 students) to 76% (19 of 25 students) in the other (P = 0.007). CONCLUSIONS: Seeing unannounced SPs had a dramatic effect on later student performance. This potentially powerful intervention could be applied to a range of clinical issues.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".