Burnout combating strategies, triggers, implications, and self-coping mechanisms among nurses working in Saudi Arabia: a multicenter, mixed methods study
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) has defined burnout as an occupational phenomenon resulting from unsuccessfully managed chronic workplace stress. The well-being of healthcare professionals is the foundation for the health of patients and organizations. In Saudi Arabia, nursing plays a pivotal role in the healthcare sector, with both local and expatriate nurses taking part in various clinical and high-pressure services. However, rapid growth in healthcare facilities, staff shortages, and patient loads are adversely impacting stress levels among nurses. METHODS: This study used a mix of research methods to survey 1,747 nurses (with a 90% response rate) from three major hospitals (King Fahad Medical City, King Saud Medical City, and King Faisal Specialist Hospital and Research Center), two smaller hospitals (Prince Mohammed Bin Abdulaziz Hospital and Al Habib Medical Group (HMG)-Al Suwaidi Hospital branch), and five primary healthcare centers, selecting participants randomly. Survey questionnaires collected data to evaluate the significance of the proposed burnout-combating strategies in relation to nursing administration, workload, and hospital administration responsibilities. Qualitative data were gathered through semi-structured interviews with 90 nurses to investigate and explore burnout triggers, implications, and coping mechanisms. FINDINGS: A majority of the nurses (87.9%) indicated that assessing their needs and listening to their feedback could help them manage and prevent burnout. In comparison, 89.7% believed that their leaders should enhance the work environment and conditions, while 87% suggested adjusting the nurse-to-patient ratio to improve patient satisfaction. Conversely, 6.8% of the participants held a negative perception that each additional patient per nurse was associated with an increase in the mortality rate, whereas 3.4% felt that granting nurses more control over their schedules and conducting departmental meetings to discuss health could help alleviate work pressure. We identified three themes: factors that trigger burnout symptoms, the implications of burnout, and suggested coping mechanisms. Additionally, they highlighted the prevalence and likelihood of burnout triggers, implications, and coping strategies, providing critical insights for nurse leaders, workload management, and hospital administrators. CONCLUSION: Nurse leaders, along with workload management strategies and hospital administrators, play a crucial role in mitigating and overcoming burnout. Establishing a healthy work environment is recognized as the most effective strategy for combating burnout, followed by implementing mental health education and training programs to enhance adaptive and cognitive resilience, promote health improvement, and strengthen resistance to burnout. Further research is needed to evaluate the effectiveness of these coping strategies for other healthcare professionals and to explore how cultural diversity, religious beliefs, and social factors may influence burnout triggers, consequences, and the development of self-coping mechanisms. IMPLICATIONS FOR PRACTICE: Healthcare leaders should remain vigilant and prioritize strengthening resilience in hospital settings. Changes in institutional policies are essential to upholding suitable staffing ratios to reduce workload stress, implementing equitable scheduling practices to enhance work-life balance, and ensuring consistent, uninterrupted breaks to promote mental and physical rejuvenation. Organizations must implement policies that directly address the factors contributing to burnout. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".