Impact of the COVID-19 Pandemic on the Wellness of Canadian Plastic Surgery Residents
Bibliographic record
Abstract
On top of preexisting burnout, depression, and anxiety among trainees, the COVID-19 pandemic has introduced novel stressors. The objectives of this study were to determine the effects of the COVID-19 pandemic on Canadian plastic surgery residents' practice, wellness, and overall training. Methods: Surveys for program directors and residents were created and disseminated to all English-speaking Canadian plastic surgery residency training programs. Survey results were pooled and presented as a percentage of responses for each question. Results: Response rates were 50% (n = 5/10) and 25% (n = 19/77) for program directors and residents, respectively. All program directors believed that the pandemic has a negative effect on resident wellness, 80% (n = 4/5) of which believed that their residents were coping effectively. They rated program support for resident wellness as neutral or supportive. Most programs (80%; n = 4/5) introduced strategies to support resident well-being. Most trainees (84%; n = 16/19) reported the pandemic as having a negative effect on their well-being, with approximately 50% endorsing worse emotional, social, psychological, and physical wellness, as well as feelings of burnout. Some reported difficulties coping (21%; n = 4/19). Residents felt that their wellness was supported externally by their own resilience (89%; n = 17/19), family members (74%; n = 14/19), friends (74%; n = 14/19), their partner (68%; n = 13/19), or co-residents (53%; n = 10/19). Internal support by their program was rated as neutral or negative (63%; n = 12/19). Conclusions: Our findings of negative effects of the COVID-19 pandemic on the wellness of Canadian plastic surgery trainees are concerning. Programs must implement appropriate identification and support strategies to improve resident well-being.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".