The relationships between nurses’ work environments and emotional exhaustion, job satisfaction, and intent to leave among nurses in Saudi Arabia
Bibliographic record
Abstract
AIMS: To examine relationships between components of nurses' work environments and emotional exhaustion, job satisfaction and intent to leave among nurses in Saudi Arabia. DESIGN: A descriptive correlational study with cross-sectional data. METHODS: Data were collected in 2017 from 497 Registered Nurses working in a large tertiary hospital in Riyadh, Saudi Arabia. Participants completed an online survey like that used in RN4Cast studies to measure nurses' perceptions of their work environments and nurse outcomes. Hierarchical linear regression and logistic regression were conducted to examine the relationships between components of nurses' work environments and nurse outcomes after controlling for nurse and patient characteristics. RESULTS: Nurse participation in hospital affairs was uniquely associated with all three nurse outcomes, whereas staffing and resource adequacy was associated with emotional exhaustion and job satisfaction, but not intent to leave. These two variables were also the components of the nursing practice environment that received the lowest ratings. Nurse manager ability, leadership and support of nurses, and nurse-physician relationships were associated with job satisfaction only. A nursing foundation for quality of care was not uniquely associated with any of the three outcomes. Finally, nurse emotional exhaustion and job satisfaction fully mediated the relationship between nurse participation in hospital affairs and intent to leave. CONCLUSION: Magnet-like work environments in Saudi Arabia are critical to recruiting and retaining nurses in a country with critical nursing shortages. IMPACT: This study addresses a gap in the literature regarding which components of the nurses' work environment are uniquely associated with emotional exhaustion, job satisfaction and intent to leave among nurses in Saudi Arabia. Study results will assist Saudi hospital administrators and nurse leaders to develop recruitment and retention strategies by focusing on those work environment components most associated with nurse outcomes: participation in hospital affairs and staffing and resource adequacy.
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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 | 0.000 |
| 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".