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 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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".