Physician Leadership and Its Effect on Physician Burnout and Satisfaction During the COVID-19 Pandemic
Bibliographic record
Abstract
Edward G Spilg,1– 3 Kylie McNeill,1 Melanie Dodd-Moher,2,4 Johanna Suzanne Dobransky,2,4 Elham Sabri,2 Jerry M Maniate,1– 3 Kathleen A Gartke2,4 1Department of Medicine, University of Ottawa, Ottawa, ON, Canada; 2Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada; 3Bruyère Research Institute, Ottawa, ON, Canada; 4Division of Orthopaedic Surgery, the Ottawa Hospital, Ottawa, ON, CanadaCorrespondence: Edward G Spilg, Department of Medicine, University of Ottawa, 1053 Carling Ave, Ottawa, ON, K1Y 4E9, Canada, Tel +1 613 798 5555, Email espilg@toh.caPurpose: Physician burnout is a global issue associated with low job satisfaction, decreased quality of patient care, reduced productivity, and early retirement from clinical practice. We sought to evaluate the impact of the leadership qualities of direct physician supervisors on the burnout and professional satisfaction of the physicians they supervise.Methods: An online survey was distributed by Email to all staff physicians practicing at a large Canadian academic tertiary care hospital. The primary outcome was the prevalence of burnout and professional satisfaction, assessed using the 2-item Maslach Burnout Inventory and a single item 5-point Likert scale rating, respectively. The secondary outcome was the relationship between composite leadership score and burnout/satisfaction, with leadership assessed by the 12-item Mayo Clinic Participatory Management Leadership Index.Results: Out of the 1176 physicians surveyed, 383 (32.6%) responded (51.2% male; 41.5% female). Overall, 41.7% of physicians reported at least one symptom of burnout (40.0% reported high emotional exhaustion; 15.3% reported high depersonalization). 40.1% of physicians reported being satisfied with the organization, 26.3% were neutral, and 33.6% were dissatisfied. On multivariate analysis adjusting for age, sex, duration of employment at the institution, and specialty, each one-point increase in composite leadership score was associated with a 3.1% decrease in the likelihood of burnout (p = 0.0017), and a 6.6% increase in the likelihood of satisfaction (p < 0.0001).Conclusion: Physician burnout is prevalent and positive leadership qualities of direct supervisors decreases the likelihood of burnout in physicians and increases the likelihood of their satisfaction with the organization.Trial Registration: ClinicalTrials.gov; Identifier: NCT04896307.Keywords: physician wellness, burnout, satisfaction, leadership
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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 | 0.003 | 0.023 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".