Assessing the magnitude of burnout among emergency nurses in Portugal
Bibliographic record
Abstract
Background: Burnout is a health condition associated with chronic work-related stress. Nurses working in hospital emergency rooms are particularly susceptible to experiencing burnout. It is crucial to understand the phenomena of burnout among emergency room nurses, particularly in light of the COVID-19 pandemic that healthcare professionals have confronted. This study aims to evaluate burnout among nurses working in emergency rooms while examining the relationship between burnout and sociodemographic and occupational variables. Methods: This descriptive, observational, and cross-sectional study utilized a web-based survey administered to 112 nurses from eight hospital emergency rooms in the Lisbon metropolitan area between November 2022 and February 2023. Burnout was measured using the Portuguese version of the Maslach Burnout Inventory (MBI), which assessed three subscales: Emotional Exhaustion (EE), Depersonalization (DP), and Personal Accomplishment (PA). The relationship between burnout and sociodemographic and professional characteristics of nurses in emergency rooms was analyzed using Chi-square tests and one-way ANOVA. Results: The prevalence of burnout was 56.6%, with 27.4% experiencing severe burnout. The three subscales of the MBI showed high prevalence rates: 49.1% for EE, 44.6% for DP, and 38.4% for low PA. Severe burnout and high EE were associated with younger age, being single, not having children, having less professional experience, less graduate training, and having more precarious employment contracts. Conclusions: Three years after the onset of the COVID-19 pandemic, the results highlight the ongoing critical situation arising from the cumulative effects of the crisis on the Portuguese healthcare system, leading to high rates of burnout and EE among emergency room nurses.
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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 | 0.002 | 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 | 0.000 | 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) | 0.001 | 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".