Association of Pediatric Resident Physician Depression and Burnout With Harmful Medical Errors on Inpatient Services
Bibliographic record
Abstract
PURPOSE: To determine whether higher rates of medical errors were associated with positive screenings for depression or burnout among resident physicians. METHOD: The authors conducted a prospective cohort study from 2011 to 2013 in seven pediatric academic medical centers in the United States and Canada. Resident physicians were screened for burnout and depression using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) and Harvard Department of Psychiatry/National Depression Screening Day Scale (HANDS). A two-step surveillance methodology, involving a research nurse and two physician reviewers, was used to measure and categorize errors. Bivariate and mixed-effects regression models were used to evaluate the relationship between burnout, depression, and rates of harmful, nonharmful, and total errors. RESULTS: A total of 388/537 (72%) resident physicians completed the MBI-HSS and HANDS surveys. Seventy-six (20%) and 178 (46%) resident physicians screened positive for depression and burnout, respectively. Screening positive for depression was associated with a 3.0-fold higher rate of harmful errors (incidence rate ratio = 2.99 [95% CI 1.40-6.36], P = .005). However, there was no statistically significant association between depression and total or nonharmful errors or between burnout and harmful, nonharmful, or total errors. CONCLUSIONS: Resident physicians with a positive depression screen were three times more likely than those who screened negative to make harmful errors. This association suggests resident physician mental health could be an important component of patient safety. If further research confirms resident physician depression increases the risk of harmful errors, it will become imperative to determine what interventions might mitigate this risk.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".