The impact of COVID-19 on nurses’ job satisfaction: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The global healthcare landscape was profoundly impacted by the COVID-19 pandemic placing nurses squarely at the heart of this emergency. This review aimed to identify the factors correlated with nurses' job satisfaction, the impact of their job satisfaction on both themselves and their patients, and to explore strategies that might have counteracted their job dissatisfaction during the COVID-19 pandemic. Methods: The Joanna Briggs Institute (JBI) methodology for systematic reviews of prevalence and incidence was used in this review. The electronic databases of CINAHL, MEDLINE, SCOPUS, PsycINFO and Academic Search Complete were searched between January 2020 to February 2023. Results: The literature review identified 23 studies from 20 countries on nurses' job satisfaction during the COVID-19 pandemic. A pooled prevalence of 69.6% of nurses were satisfied with personal, environmental, and psychological factors influencing their job satisfaction. Job satisfaction improved psychological wellbeing and quality of life, while dissatisfaction was linked to turnover and mental health issues. Conclusion: This systematic review elucidates key factors impacting nurses' job satisfaction during the COVID-19 pandemic, its effects on healthcare provision, and the potential countermeasures for job dissatisfaction. Core influences include working conditions, staff relationships, and career opportunities. High job satisfaction correlates with improved patient care, reduced burnout, and greater staff retention. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023405947, the review title has been registered in PROSPERO and the registration number is CRD42023405947.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".