Transparency When Things Go Wrong: Physician Attitudes About Reporting Medical Errors to Patients, Peers, and Institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Transparent communication after medical error includes disclosing the mistake to the patient, discussing the event with colleagues, and reporting to the institution. Little is known about whether attitudes about these transparency practices are related. Understanding these relationships could inform educational and organizational strategies to promote transparency. METHODS: We analyzed responses of 3038 US and Canadian physicians to a medical error communication survey. We used bivariate correlations, principal components analysis, and linear regression to determine whether and how physician attitudes about transparent communication with patients, peers, and the institution after error were related. RESULTS: Physician attitudes about disclosing errors to patients, peers, and institutions were correlated (all P's < 0.001) and represented 2 principal components analysis factors, namely, communication with patients and communication with peers/institution. Predictors of attitudes supporting transparent communication with patients and peers/institution included female sex, US (vs Canadian) doctors, academic (vs private) practice, the belief that disclosure decreased likelihood of litigation, and the belief that system changes occur after error reporting. In addition, younger physicians, surgeons, and those with previous experience disclosing a serious error were more likely to agree with disclosure to patients. In comparison, doctors who believed that disclosure would decrease patient trust were less likely to agree with error disclosure to patients. Previous disclosure education was associated with attitudes supporting greater transparency with peers/institution. CONCLUSIONS: Physician attitudes about discussing errors with patients, colleagues, and institutions are related. Several predictors of transparency affect all 3 practices and are potentially modifiable by educational and institutional strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it