Moral Distress, Burnout, Turnover Intention, and Coping Strategies among Korean Nurses during the Late Stage of the COVID-19 Pandemic: A Mixed-Method Study
Bibliographic record
Abstract
The COVID-19 pandemic has exacerbated the difficulties nurses face, resulting in higher turnover rates and workforce shortages. This study investigated the relationships between nurses' moral distress, burnout, and turnover intention during the last stage of the COVID-19 pandemic. It also explored the coping strategies nurses use to mitigate moral distress. Utilizing a mixed-method approach, this study analyzed data from 307 nurses caring for patients with COVID-19 in acute care hospitals through an online survey conducted in November 2022. Our data analysis encompassed quantitative methods, including descriptive statistics and path analysis, using a generalized structural equation model. For the qualitative aspect, we examined open-ended responses from 246 nurses using inductive content analysis. The quantitative findings revealed that nurses' moral distress had a significant direct effect on turnover intention. In addition, burnout significantly mediated the relationship between moral distress and turnover intention. Qualitative analyses contextualized the relationships uncovered in the quantitative analyses. The qualitative analysis identified various positive and negative coping strategies. Positive strategies included a commitment to minimize COVID-19 transmission risks, adopting a holistic approach amidst the challenges posed by the pandemic, voicing concerns for patient safety, engaging in continuous learning, and prioritizing self-care. Conversely, negative strategies involved adopting avoidance behaviors stemming from feelings of powerlessness and adopting a passive approach to one's role. Notably, some participants shifted from positive to negative coping strategies because of institutional barriers and challenges. The findings underscore the importance for hospital administrators and nurse managers to acknowledge the impact of the pandemic-related challenges encountered by nurses and recognize the link among moral distress, burnout, and turnover intention. It highlights the essential role of organizational and managerial support in fostering effective coping strategies among nurses to address moral distress.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".