Levels of Burnout and Resilience Among Nursing Staff at a Public Sector Tertiary Hospital in Swat.
Bibliographic record
Abstract
Background: Burnout refers to a state of physical, emotional, and psychological fatigue resulting from over time exposure to stress, particularly in demanding domains such as health care. While coping builds the ability to withstand stress and cope with pressure, resilience is the ability to bounce back to normal wellbeing after a stressful period easily. In its simplest terms, nurses—counted among the fundamental members of the health care system offer crucial patient care in sometimes testing circumstances. Aim: The study aimed to assess burnout and resilience among nurses working in a public-sector tertiary care hospital in District Swat.Methods: The study employed an analytical cross-sectional design to assess burnout and resilience among nurses in a tertiary care hospital in Swat. A convenient sampling technique selected 84 nurses, meeting specific inclusion and exclusion criteria. Data were collected using the Maslach Burnout Inventory (MBI) and Connor-Davidson Resilience Scale (CD-RISC) over two weeks and analyzed via SPSS (version 26). Descriptive statistics and Chi-square tests examined burnout, resilience, and their associations.Results: The study assessed burnout and resilience among 84 nurses in a tertiary care hospital. Most participants were young females with moderate levels of burnout and resilience. The most prevalent burnout dimensions were emotional exhaustion, depersonalization, and reduced personal accomplishment. Significant associations were found between resilience and all burnout dimensions, highlighting the importance of resilience in reducing burnout.Conclusion: The study found moderate levels of burnout among nurses, especially in emotional exhaustion and personal accomplishment. Most nurses demonstrated moderate resilience, but a significant portion showed low resilience. A strong association between burnout and resilience suggests that improving resilience could help reduce burnout and enhance nurses' wellbeing in high-stress environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".