Characterizing Resident Preferences for Faculty Involvement and Support in Disclosing Medical Errors to Patients
Bibliographic record
Abstract
BACKGROUND: Residents may be commonly involved with medical errors and need faculty support when disclosing these to patients. OBJECTIVE: We characterized residents' preferences for faculty involvement and support during the error disclosure process. METHODS: We surveyed residents from internal medicine, pediatrics, emergency medicine, general and orthopedic surgery, and obstetrics and gynecology residency programs at the University of Toronto in 2014-2015 about their preferences for faculty involvement across a variety of different error scenarios (ie, error type, severity, and proximity) and for elements of support they perceive to be most helpful during the disclosure process. RESULTS: Over 90% of the 192 respondents (N = 538, response rate 36%) wanted direct involvement in the error disclosure process, irrespective of type or severity of the error. Residents were relatively comfortable disclosing prescription and communication errors without direct faculty involvement but preferred faculty involvement when disclosing diagnostic and management errors. When errors were severe, many residents still wanted to be involved but preferred having faculty lead the disclosure. Residents particularly wanted to participate in the process when they felt responsible for the error. Residents highly valued receiving faculty advice on how to manage consequences and how to prevent future errors in preparing for disclosure, as well as receiving postdisclosure feedback and personal support. CONCLUSIONS: Residents are willing participants in the error disclosure process and have specific preferences for faculty involvement and support. These findings can inform faculty development to ensure appropriate support and supervision for residents when disclosing errors to patients.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".