How trainees would disclose medical errors: educational implications for training programmes
Bibliographic record
Abstract
OBJECTIVES: The disclosure of harmful errors to patients is recommended, but appears to be uncommon. Understanding how trainees disclose errors and how their practices evolve during training could help educators design programmes to address this gap. This study was conducted to determine how trainees would disclose medical errors. METHODS: We surveyed 758 trainees (488 students and 270 residents) in internal medicine at two academic medical centres. Surveys depicted one of two harmful error scenarios that varied by how apparent the error would be to the patient. We measured attitudes and disclosure content using scripted responses. RESULTS: Trainees reported their intent to disclose the error as 'definitely' (43%), 'probably' (47%), 'only if asked by patient' (9%), and 'definitely not' (1%). Trainees were more likely to disclose obvious errors than errors that patients were unlikely to recognise (55% versus 30%; p < 0.01). Respondents varied widely in the type of information they would disclose. Overall, 50% of trainees chose to use statements that explicitly stated that an error rather than only an adverse event had occurred. Regarding apologies, trainees were split between conveying a general expression of regret (52%) and making an explicit apology (46%). Respondents at higher levels of training were less likely to use explicit apologies (trend p < 0.01). Prior disclosure training was associated with increased willingness to disclose errors (odds ratio 1.40, p = 0.03). CONCLUSIONS: Trainees may not be prepared to disclose medical errors to patients and worrisome trends in trainee apology practices were observed across levels of training. Medical educators should intensify efforts to enhance trainees' skills in meeting patients' expectations for the open disclosure of harmful medical errors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".