The Impact of the COVID-19 Pandemic on Resident Physicians Well-Being in the Surgical and Primary Care Specialties in the United States and Canada
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic disrupted the professional, social, and spiritual activities of resident physicians around the world, impacting wellness and personal relationships. Moreover, social distancing caused significant limitations or shutdown of places of worship, including churches, synagogues, mosques, etc. Our goal was to survey resident physicians in primary care and surgical subspecialties in the United States (U.S.) and Canada and to examine the effect of the COVID-19 pandemic on their well-being. METHODS: An international cross-sectional study was performed in November 2020, using an anonymous survey of programs in the U.S. and Canada, containing 20 questions to assess the impact of the pandemic on resident participation in social and spiritual activities and the effects on their wellness, and personal relationships. The emails with survey links attached were sent to individual program coordinators from accredited residency training programs in the United States and Canada. This consisted of programs accredited by the American Osteopathic Association (AOA), The Royal College of Physicians and Surgeons of Canada (RCPSC), and the Accreditation Council of Graduate Medical Education (ACGME). The survey was evenly divided among surgical programs (General Surgery, Neurological Surgery, Orthopedic Surgery, Urological Surgery, and Integrated Surgical Residency Programs such as Plastic Surgery, Cardiothoracic Surgery, Pediatric Surgery, and Vascular Surgery) as well as primary care programs (Internal Medicine and Family Medicine). RESULTS: A total of 196 residents, 60 primary care residents, and 136 surgery residents participated in the study. Ninety-six participants (49%) were female, and 98 of the participants (50%) were male, with the remainder two residents identifying as "Other." Of the primary care residents, the majority (39, 65%) were female. Conversely, the majority (77, 57%) of surgery residents were male. CONCLUSION: The COVID-19 pandemic has affected the social lives, relationships, and spiritual well-being of both surgical and primary care resident physicians. However, primary care residents reported significantly greater engagement in personal relationships and were more likely to express feelings of mental and physical exhaustion, prohibiting social attendance.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".