US and Canadian Physicians' Attitudes and Experiences Regarding Disclosing Errors to Patients
Bibliographic record
Abstract
BACKGROUND: Patients are often not told about harmful medical errors. The malpractice environment is considered a major determinant of physicians' willingness to disclose errors to patients. Yet, little is known about the malpractice environment's actual effect on physicians' error disclosure attitudes and experiences. METHODS: Mailed survey of 2637 physicians (62.9% response rate) in the United States (Missouri and Washington) and Canada, countries with different malpractice environments. RESULTS: Physicians' error disclosure attitudes and experiences were similar across country and specialty. Of the physicians, 64% agreed that errors are a serious problem. However, 50% disagreed that errors are usually caused by system failures. Ninety-eight percent endorsed disclosing serious errors to patients and 78% supported disclosing minor errors; 74% thought disclosing a serious error would be very difficult. Fifty-eight percent had disclosed a serious error to a patient, and 85% were satisfied with the disclosure, and 66% agreed that disclosing a serious error reduces malpractice risk. Respondents' estimates of the probability of lawsuits were not associated with their support for disclosure. The belief that disclosure makes patients less likely to sue (odds ratio, 1.58), not being in private practice (odds ratio, 1.47), being Canadian (odds ratio, 1.43), and being a surgeon (odds ratio, 1.26) were independently associated with higher support for disclosing serious errors. CONCLUSIONS: US and Canadian physicians' error disclosure attitudes and experiences are similar despite different malpractice environments, and reveal mixed feelings about disclosing errors to patients. The medical profession should address the barriers to transparency within the culture of medical and surgical specialties.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".