Learning Through Experience: Influence of Formal and Informal Training on Medical Error Disclosure Skills in Residents
Bibliographic record
Abstract
ABSTRACT Background Residents' attitudes toward error disclosure have improved over time. It is unclear whether this has been accompanied by improvements in disclosure skills. Objective To measure the disclosure skills of internal medicine (IM), paediatrics, and orthopaedic surgery residents, and to explore resident perceptions of formal versus informal training in preparing them for disclosure in real-world practice. Methods We assessed residents' error disclosure skills using a structured role play with a standardized patient in 2012–2013. We compared disclosure skills across programs using analysis of variance. We conducted a multiple linear regression, including data from a historical cohort of IM residents from 2005, to investigate the influence of predictor variables on performance: training program, cohort year, and prior disclosure training and experience. We conducted a qualitative descriptive analysis of data from semistructured interviews with residents to explore resident perceptions of formal versus informal disclosure training. Results In a comparison of disclosure skills for 49 residents, there was no difference in overall performance across specialties (4.1 to 4.4 of 5, P = .19). In regression analysis, only the current cohort was significantly associated with skill: current residents performed better than a historical cohort of 42 IM residents (P < .001). Qualitative analysis identified the importance of both formal (workshops, morbidity and mortality rounds) and informal (role modeling, debriefing) activities in preparation for disclosure in real-world practice. Conclusions Residents across specialties have similar skills in disclosure of errors. Residents identified role modeling and a strong local patient safety culture as key facilitators for disclosure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".