Teaching Medical Error Disclosure to Physicians-in-Training
Bibliographic record
Abstract
PURPOSE: This scoping review identified published studies of error disclosure curricula targeting physicians-in-training (residents or medical students). METHOD: In 2011, the authors searched electronic databases (e.g., MEDLINE, EMBASE, ERIC) for eligible studies published between 1960 and July 2011. From the studies that met their inclusion criteria, they extracted and summarized key aspects of each curriculum (e.g., level of learner, program discipline) and educational features (e.g., curriculum design, teaching and assessment methods, and learner outcomes). RESULTS: The authors identified 21 studies that met their inclusion criteria. These studies described 19 error disclosure curricula, which were either a stand-alone educational activity, part of a larger curriculum in patient safety or communication skills, or part of simulation training. Most curricula consisted of a brief, single encounter, combining didactic lectures or small-group discussions with role-play. Fourteen studies described learners' self-reported improvements in knowledge, skills, and attitudes. Five studies used a structured assessment and reported that learners' error disclosure skills improved after completing the curriculum; however, these studies were limited by their small to medium sample size and lack of assessment of skills retention. Attempts to assess the change in learners' error disclosure behavior in the clinical context were limited. CONCLUSIONS: Studies of existing error disclosure curricula demonstrate improvements in learners' knowledge, skills, and attitudes. A greater emphasis is needed on the more rigorous assessment of skills acquisition and behavior change to determine whether formal training leads to long-term effects on learner outcomes that translate into real-world clinical practice.
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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 | 0.018 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".