COVID-19-Related Occupational Burnout and Moral Distress among Nurses: A Rapid Scoping Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is placing unprecedented pressure on a nursing workforce that is already under considerable mental strain due to an overloaded system. Convergent evidence from the current and previous pandemics indicates that nurses experience the highest levels of psychological distress compared with other health professionals. Nurse leaders face particular challenges in mitigating risk and supporting nursing staff to negotiate moral distress and fatigue during large-scale, sustained crises. Synthesizing the burgeoning literature on COVID-19-related burnout and moral distress faced by nurses and identifying effective interventions to reduce poor mental health outcomes will enable nurse leaders to support the resilience of their teams. AIM: This paper aims to (1) synthesize existing literature on COVID-19-related burnout and moral distress among nurses and (2) identify recommendations for nurse leaders to support the psychological needs of nursing staff. METHODS: Comprehensive searches were conducted in Medline, Embase and PsycINFO (via Ovid); CINAHL (via EBSCOHost); and ERIC (via ProQUEST). The rapid review was completed in accordance with the World Health Organization Rapid Review Guide. KEY FINDINGS: Thematic analysis of selected studies suggests that nurses are at an increased risk for stress, burnout and depression during the ongoing COVID-19 pandemic. Younger female nurses with less clinical experience are more vulnerable to adverse mental health outcomes.
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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 | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".