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.
Copyright: © 2023 Yuan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.