Moral distress and burnout in internal medicine residents
Bibliographic record
Abstract
BACKGROUND: Residents frequently encounter situations in their workplace that may induce moral distress or burnout. The objective of this study was to measure overall and rotation-specific moral distress and burnout in medical residents, and the relationship between demographics and moral distress and burnout. METHODS: The revised Moral Distress Scale and the Maslach Burnout Inventory (Human Service version) were administered to Internal Medicine residents in the 2013-2014 academic year at the University of British Columbia. RESULTS: Of the 88 residents, 45 completed the surveys. Participants (mean age 30+/-3; 46% male) reported a median moral distress score (interquartile range) of 77 (50-96). Twenty-six percent of residents had considered quitting because of moral distress, 21% had a high level of burnout, and only 5% had a low level of burnout. Moral distress scores were highest during Intensive Care Unit (ICU) and Clinical Teaching Unit (CTU) rotations, and lowest during elective rotations (p<0.0001). Women reported higher emotional exhaustion. Moral distress was associated with depersonalization (p=0.01), and both moral distress and burnout were associated with intention to leave the job. CONCLUSION: Internal Medicine residents report moral distress that is greatest during ICU and CTU rotations, and is associated with burnout and intention to leave the job.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".