Impact of the COVID-19 Pandemic on Clinical Practice and Work–Life Integration Experienced by Academic Medical Faculty
Bibliographic record
Abstract
Introduction: Before the COVID-19 pandemic, reported physician burnout was endemic in North America, with rates as high as 51%. The pandemic placed an increased demand on physicians’ time both in their work and home lives. We sought to identify the frequency of burnout in a large academic institution and its impact on clinical practice, non-clinical work, and home life. Methods: All academic physicians and non-physician faculty members in the Department of Medicine (DOM) at McMaster University were invited to participate in an anonymous survey between January 22 and February 21, 2021. The survey elicited information on how clinical practice, work, and home life changed throughout the pandemic. Responses to questions were captured on a 1-to-5 Likert scale. Descriptive statistics were calculated and the Mann–Whitney U-test was used to determine statistical significance (p < 0.05). The results were compared to the 2019 DOM survey which included a question on burnout. Results: Among 330 faculty, 76.7% completed the survey. The reported burnout was high at 75.9%, affecting women to a greater extent than men (82.5% vs 70.4%, p < 0.01). Early career faculty also reported proportionally more burnout (83.5% vs 65.7%; p < 0.001). Medical-legal liability of phone-based care was a concern for 48% of physicians. The reported hours of work per day were significantly higher amongst women than men compared to pre-pandemic (80.4% vs 58.0%; p < 0.001). Loneliness (64.1% vs 51.4%; p < 0.05) and hours spent on caring for dependents (54.5% vs 31.1%, p < 0.01) were higher for women versus men. Both genders reported career fulfillment and research productivity were overall lower by 51.2% and 52.3%, respectively. Conclusions: The COVID-19 pandemic has increased burnout amongst DOM academic faculty, and disproportionately affected women and early career faculty. A thoughtful systems-level approach, with dedicated resources, is needed to address the impact of the COVID-19 pandemic on medical faculty.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".