Evidence map›Paper›PMID 39090650›Full record

ArticleBMC health services research2024

A Bayesian approach to reveal the role of psychological factors on turnover intention among nurses during the COVID-19 pandemic.

Saeid Yazdanirad, Mojtaba Haghighat, Mahsa Jahadinaeini, Amirhossein Khoshakhlagh, Seyedmahdi Mousavi

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Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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5 authors.

Saeid YazdaniradSchool of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran.
Mojtaba HaghighatBehbahan university of medical sciences, Behbahan, Iran.
Mahsa JahadinaeiniDepartment of Occupational Health Engineering, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Amirhossein KhoshakhlaghDepartment of Occupational Health Engineering, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran.
Seyedmahdi MousaviBehbahan university of medical sciences, Behbahan, Iran. mahdi.mousavi90@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTurnover intention is considered a significant challenge for healthcare and treatment organizations. The challenging conditions of treating COVID-19 patients and the physical and mental stress imposed on nurses during the pandemic may lead them to leave their jobs. The present study aimed to determine the role of psychological factors (general health, mental workload, work-family conflicts, and resilience) on turnover intention using a Bayesian approach during the COVID-19 pandemic.

methodsThe present cross-sectional study was carried out during the winter of 2021 at three hospitals in Khuzestan Province, Iran. To collect data for this investigation, 300 nurses were chosen based on Cochran's formula and random sampling technique. Seven questionnaires, including General Health, Mental Workload, Work-Family Conflict, Resilience, Job Stress, Fear of COVID-19, and Turnover Intention Questionnaires. Bayesian Networks (BNs) were used to draw probabilistic and graphical models. A sensitivity analysis also was performed to study the effects of the variables. The GeNIe academic software, version 2.3, facilitated the examination of the Bayesian network.

resultsThe statistically significant associations occurred between the variables of fear of COVID-19 and job stress (0.313), job stress and turnover intention (0.302), and resilience and job stress (0.298), respectively. Job stress had the highest association with the fear of COVID-19 (0.313), and resilience had the greatest association with the work-family conflict (0.296). Also, the association between turnover intention and job stress (0.302) was higher than the association between this variable and resilience (0.219). At the low resilience and high job stress with the probability of 100%, the turnover intention variable increased by 20%, while at high resilience and low job stress with the probability of 100%, turnover intention was found to decrease by 32%.

conclusionIn general, the results showed that four psychological factors affect job turnover intention. However, the greatest impact was related to job stress and resilience. These results can be used to manage job turnover intention in medical environments, especially in critical situations such as COVID-19.

Indexed as

Bayes TheoremCOVID-19IntentionPandemicsPersonnel TurnoverAdultCross-Sectional StudiesFemaleHumansIranJob SatisfactionMaleNursing Staff, HospitalOccupational StressResilience, PsychologicalSARS-CoV-2Bayesian approachCOVID-19 pandemicJob stressPsychological factorsResilienceTurnover intention

Identifiers

PMID39090650
PMCPMC11295345

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.