Psychological Health of Surgeons in a Time of COVID-19
Bibliographic record
Abstract
OBJECTIVE: To assess the degree of psychological impact among surgical providers during the COVID-19 pandemic. SUMMARY OF BACKGROUND DATA: The COVID-19 pandemic has extensively impacted global healthcare systems. We hypothesized that the degree of psychological impact would be higher for surgical providers deployed for COVID-19 work, certain surgical specialties, and for those who knew of someone diagnosed with, or who died, of COVID-19. METHODS: We conducted a global web-based survey to investigate the psychological impact of COVID-19. The primary outcomes were the depression anxiety stress scale-21 and Impact of Event Scale-Revised scores. RESULTS: A total of 4283 participants from 101 countries responded. 32.8%, 30.8%, 25.9%, and 24.0% screened positive for depression, anxiety, stress, and PTSD respectively. Respondents who knew someone who died of COVID-19 were more likely to screen positive for depression, anxiety, stress, and PTSD (OR 1.3, 1.6, 1.4, 1.7 respectively, all P < 0.05). Respondents who knew of someone diagnosed with COVID-19 were more likely to screen positive for depression, stress, and PTSD (OR 1.2, 1.2, and 1.3 respectively, all P < 0.05). Surgical specialties that operated in the head and neck region had higher psychological distress among its surgeons. Deployment for COVID- 19-related work was not associated with increased psychological distress. CONCLUSIONS: The COVID-19 pandemic may have a mental health legacy outlasting its course. The long-term impact of this ongoing traumatic event underscores the importance of longitudinal mental health care for healthcare personnel, with particular attention to those who know of someone diagnosed with, or who died of COVID-19.
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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 | 0.006 |
| 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) | 0.004 | 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".