Pediatric Residentsʼ Decision-Making Around Disclosing and Reporting Adverse Events: The Importance of Social Context
Bibliographic record
Abstract
PURPOSE: Although experts advise disclosing medical errors to patients, individual physicians' different levels of knowledge and comfort suggest a gap between recommendations and practice. This study explored pediatric residents' knowledge and attitudes about disclosure. METHOD: In 2006, the authors of this single-center, mixed-methods study surveyed 64 pediatric residents at the University of Toronto and then held three focus groups with a total of 24 of those residents. RESULTS: Thirty-seven (58%) residents completed questionnaires. Most agreed that medical errors are one of the most serious problems in health care, that errors should be disclosed, and that disclosure would be difficult. When shown a scenario involving a medical error, over 90% correctly identified the error, but only 40% would definitely disclose it. Most would apologize, but far fewer would acknowledge harm if it occurred or use the word "mistake." Most had witnessed or performed a disclosure, but only 40% reported receiving teaching on disclosure. Most reported experiencing negative effects of errors, including anxiety and reduced confidence. Data from the focus groups emphasized the extent to which residents consider contextual information when making decisions around disclosure. Themes included their or their team's degree of responsibility for the error versus others, quality of team relationships, training level, existence of social boundaries, and their position within a hierarchy. CONCLUSIONS: These findings add to the understanding of facilitators and inhibitors of error disclosure and reporting. The influence of social context warrants further study and should be considered in medical curriculum design and hospital guideline implementation.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".