Skills of Internal Medicine Residents in Disclosing Medical Errors: A Study Using Standardized Patients
Bibliographic record
Abstract
PURPOSE: To determine internal medicine (IM) residents' ability to disclose a medical error using standardized patients (SPs) and to survey residents' experiences of disclosure. METHOD: In 2005, 42 second-year IM residents at the University of Toronto participated in the study. Each resident disclosed one medical error (insulin overdose) to an SP. The SP and a physician observer scored performance using a rating scale (1 = not performed, 2 = performed somewhat, and 3 = performed well) that measures error disclosure on five specific component skills and that provides an overall assessment score (scored on a five-point scale, 5 = high). Residents also completed a questionnaire. RESULTS: The mean scores on the five components were explanation of medical facts (2.60), honesty (2.31), empathy (2.47), future error prevention (1.99), and general communication skills (2.47). The residents' mean overall disclosure score was 3.53. Although 27 of 42 residents (64%) reported previous experience in disclosing an error to a patient during their training, only 7 (27%) of these residents reported receiving any feedback about their performance. Of 41 residents, 21 (51%) had received some prior training in disclosure, and 38 (93%) thought additional training would be useful and relevant. CONCLUSIONS: Disclosing medical error is now a standard practice. Experience with medical error begins early in training, and preparing trainees to discuss these errors is essential. Areas exist for improvement in residents' disclosure abilities, particularly regarding the prevention of future errors. Curricula to increase residents' skills and comfort in disclosure need to be implemented. Most residents would welcome further training.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".