Burnout, Depression, and Stress in Emergency Department Nurses and Physicians and the Impact on Private and Work Life: A Systematic Review
Bibliographic record
Abstract
Objectives: In today's fast-paced world, work-related stress is a prevalent problem, particularly among health care professionals in high-pressure environments such as emergency departments (EDs). This stress can lead to mental health disorders, such as depression and burnout, affecting job performance, patient care, and the quality of professional and private life. This systematic review aimed to investigate the prevalence of burnout, depression, and stress among ED nurses and physicians and the impact of these conditions on personal and professional quality of life (QoL). Methods: The systematic literature search covered PubMed, PsycINFO, Embase, and grey literature databases. Articles were included if they were published in English or German by 31 January 2020, focused on ED physicians or nurses, and examined burnout, depression, or stress and its impact on professional or personal QoL. Quality assessment of the included studies was performed using a modified version of the Newcastle-Ottawa Scale. Results: The systematic search resulted in 893 articles, of which 11 met the inclusion criteria. All reviewed studies had a cross-sectional study design and were of low to moderate quality. Depression, burnout, and stress were prevalent among ED physicians, ranging from 15.5% to 19.3%, 18% to 71.4%, and 19.5% to 22.7%, respectively. These were associated with lower job satisfaction in ED physicians, while findings in ED nurses also showed a considerable rate of burnout with an inverse association with compassion satisfaction. Burnout and stress were significantly associated with intentions to quit emergency medicine in ED physicians, whereas no association was found for depression. In addition, burnout showed a negative relationship to work-life balance and QoL, while higher stress levels were associated with lower life satisfaction in ED physicians. Conclusion: Our review underlines the high prevalence of stress, depression, and burnout among ED health care professionals and their potential negative impact on private and professional life, emphasizing the need for targeted support and interventions to enhance resilience, reduce stress, and prevent the onset or deterioration of mental health diseases. This, in turn, can contribute to maintaining and strengthening the already limited human resources in EDs, ensuring the quality of patient care, and strengthening health care systems.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".