Mental health variables associated with job satisfaction among nurses: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Nurses play a crucial role in healthcare, but increasing job dissatisfaction is raising concerns about its impact on patient care. Mental health problems are a key factor contributing to this dissatisfaction. This systematic review and meta-analysis aim to identify mental health variables associated with nurses' job satisfaction.Methods Following PRISMA guidelines, a comprehensive search was conducted in CINAHL, PubMed, MEDLINE, EMBASE, and PsycINFO (October 1976–December 2023). Two researchers independently assessed study eligibility, and study quality was evaluated using the Newcastle-Ottawa Scale. A random-effects model was used to pool correlation coefficients from 112 studies.Results The meta-analysis found a small negative correlation between mental health problems and job satisfaction (r = −0.25). Emotional exhaustion, burnout, stress, depersonalization, depression, and anxiety were all modestly but significantly negatively correlated with job satisfaction.Conclusion This meta-analysis highlights the significant negative impact of mental health problems on nurses' job satisfaction. Healthcare institutions must prioritize nurses' mental well-being as it directly affects job satisfaction, nurse retention, and the quality of patient care.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".