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Shift work—especially during night hours—adversely affects nurses’ cognitive and motor performance, potentially compromising patient safety. Variations in shift duration and rotation patterns contribute to these effects. Implementing evidence-based strategies such as optimized scheduling, structured rest breaks, and supportive work environments may mitigate performance declines. These findings highlight the importance of organizational policies aimed at protecting both healthcare workers and patient outcomes.
A comprehensive search across PubMed, Cochrane Library, and Web of Science identified 22 studies with 224 comparison data points for inclusion. Study quality was assessed using the ROBINS-I tool across seven bias domains. Analyses were performed using Python, applying random-effects models to account for heterogeneity (Cochran's Q, I 2), with Egger's test used for publication bias assessment.
Day shifts led to a small performance decline (Hedges' g = 0.238, 95% CI [0.155, 0.321]), while night shifts caused a more substantial decline (Hedges' g = 0.386, 95% CI: 0.320 to 0.451). Motor performance across all shift types showed a moderate effect size (Hedges' g = 0.326, 95% CI [0.210, 0.442]). Comparing day shifts to nonstandard shifts, a small effect size (Hedges' g = 0.220, 95% CI [0.171, 0.269]) highlighted reduced performance under irregular shift conditions. High heterogeneity in night shifts (I 2 = 86.8%) suggested variability in study designs and methodologies.
Shift work, particularly night shifts, negatively impacts cognitive and motor performance, posing risks to clinical safety. The variability in shift durations (6–17 h) and different shift rotation strategies contributed to heterogeneity. Targeted interventions, including optimized scheduling, adequate rest breaks, and supportive workplace practices, are needed to mitigate negative effects. This meta-analysis provides evidence-based insights into the detrimental effects of shift work on nurses' performance, supporting the development of policies and strategies to promote safer clinical environments and enhance healthcare quality.
Trial Registration: PROSPERO
The COVID-19 pandemic has caused an increase in the workload of nurses and changes in working conditions. Stress and the increase in workload during the COVID-19 pandemic had a negative effect on nurses' intention to leave. This study aimed to determine the current rate of intention to leave the job among nurses during the COVID-19 outbreak by conducting a rapid systematic review and meta-analysis.
The review procedure was conducted by the PRISMA criteria. The researchers searched PubMed and Web of Science databases for studies providing the rate of nurses' intent to leave, published until 31 December 2021. Heterogeneity was assessed using the I2 test, and publication bias was measured by Egger's test.
The estimated overall intent to leave the profession among nurses during the COVID-19 pandemic was 31.7% (95% CI: 25%–39%) with significant heterogeneity (Q test: 188.9; p = 0.0001; I2: %95.2; Tau 2: 0.225). Additionally, Egger's regression test suggested no publication bias for estimating the pooled rate of nurses' intent to leave during the COVID-19 outbreak.
Since the research is a meta-analysis study, a literature review model was used. Ethics committee approval was not obtained because the literature review did not directly affect humans and animals.
This study showed that approximately one-third of nurses working during the COVID-19 pandemic had thoughts about intending to leave their job. The findings indicate the need for strategies involving precautions and solutions to minimise the psychological impacts of COVID-19 among nurses.
In this period when the global nurse crisis exists, it is of great importance for institutions to retain their nurse workforce. There is an urgent need to prepare nurses to cope better with COVID-19 pandemic. Identification of risk factors for intention to leave could be a significant weapon giving nurses and healthcare systems the ability to response in a better way against the following COVID-19 waves in the near future.
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