Nurses' and Physicians' Distress, Burnout, and Coping Strategies During COVID-19: Stress and Impact on Perceived Performance and Intentions to Quit
Bibliographic record
Abstract
INTRODUCTION: Health care providers (HCPs) have experienced more stress and burnout during COVID-19 than before. We compared sources and levels of stress, distress, and approaches to coping between nurses and physicians, and examined whether coping strategies helped mitigate the negative impact of stress and intentions to quit. METHODS: Using a cross-sectional study design, burnout was measured with the Maslach Burnout Inventory. Psychological distress was measured using the Depression, Anxiety, and Stress Scale. A self-reported survey was used to evaluate stressors, impact on perceived performance, and intentions to quit. The data were analyzed using t-tests and linear regression models. RESULTS: Responses of 119 HCPs were analyzed. Findings suggest that (1) compared to physicians, nurses experienced a higher level of distress and burnout, and used more maladaptive coping strategies. (2) Both nurses and physicians experienced more distress and burnout during COVID-19 than before. (3) Adaptive coping strategies moderated the negative impact of stress on work performance (4) Adaptive coping strategies moderated the negative effect of stress on burnout, which in turn reduced intentions to quit. Stress negatively impacted work performance and burnout only for those with low, but not high, levels of adaptive coping strategies. DISCUSSION: The current findings of HCPs' challenges, risks, and protective factors provide valuable information (1) on COVID-19's impact on HCPs, (2) to guide the distribution of institutional supportive efforts and recommend adaptive coping strategies, and (3) to inform medical education, such as resilience training, focusing on adaptive coping approaches.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".