Longitudinal Assessment of Moral Distress and Burnout during Pediatric Residency in a Canadian Site: Low Prevalence but Notable Risk of Attrition
Bibliographic record
Abstract
Introduction: Although more than half of pediatric residents report burnout, the incidence of moral distress, the course of moral distress and burnout during residency, and associations between this course and personal characteristics of residents are unknown. The purpose of this work was to examine the incidence, temporal course, and associations of moral distress and burnout in pediatric residents. Methods: In the pediatric training program of British Columbia Children’s Hospital, all residents were invited to complete the Moral Distress Scale-Revised thrice yearly, and the Maslach Burnout Inventory annually, between July 2016 and October 2018. In addition, residents reported a measure of moral distress for each rotation. Responses were tracked longitudinally using a unique identifier for each resident. We used longitudinal mixed effect modeling and generalized estimating equations to account for clustering of data. Results: A total of 86/101 residents completed at least one sequence of the surveys. The average moral distress score was 20 (maximum possible: 336), but 10% of respondents had considered leaving residency in the past due to moral distress. Highest levels of moral distress occurred after international and intensive care rotations. Seven percent of respondents met criteria for burnout, but female residents reported higher burnout scores than males (p = 0.04). Conclusion: Although moral distress and prevalence of burnout are low in pediatric residents at this institution, moral distress contributes to potential attrition. High-acuity rotations are associated with increased levels of moral distress.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".