Burnout and fatigue amongst internal medicine residents: A cross-sectional study on the impact of alternative scheduling models on resident wellness
Bibliographic record
Abstract
BACKGROUND: Fatigue and burnout are prevalent among resident physicians across Canada. Shifts exceeding 24 hours are commonly purported as detrimental to resident health and performance. Residency training programs have employed strategies towards understanding and intervening upon the complex issue of resident fatigue, where alternative resident scheduling models have been an area of active investigation. This study sought to characterize drivers and outcomes of fatigue and burnout amongst internal medicine residents across different scheduling models. METHODS: We conducted cross-sectional surveys were among internal medicine resident physicians at the University of Alberta. We collected anonymized socioeconomic demographics and medical education background, and estimated associations between demographic or work characteristics and fatigue and burnout outcomes. RESULTS: Sixty-nine participants competed burnout questionnaires, and 165 fatigue questionnaires were completed (response rate of 48%). The overall prevalence of burnout was 58%. Lower burnout prevalence was noted among respondents with dependent(s) (p = 0.048), who identified as a racial minority (p = 0.018), or completed their medical degree internationally (p = 0.006). The 1-in-4 model was associated with the highest levels of fatigue, reported increased risk towards personal health (OR 4.98, 95%CI 1.77-13.99) and occupational or household harm (OR 5.69, 95%CI 1.87-17.3). Alternative scheduling models were not associated with these hazards. CONCLUSIONS: The 1-in-4 scheduling model was associated with high rates of resident physician fatigue, and alternative scheduling models were associated with less fatigue. Protective factors against fatigue are best characterized as strong social supports outside the workplace. Further studies are needed to characterize the impacts of alternative scheduling models on resident education and patient safety.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".