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A Conceptual Model of Nurses' Turnover Intention.

Eva SmokrovićTomislav KizivatAntun BajanKrešimir ŠolićZvjezdana GvozdanovićNikolina FarčićBoštjan Žvanut
Published in: International journal of environmental research and public health (2022)
The World Health Organisation predicts a lack of 15 million health professionals by 2030. The lack of licenced professionals is a problem that keeps emerging and is carefully studied on a global level. Strategic objectives aimed at stimulating employment, improving working conditions, and keeping the nurses on board greatly depends on identifying factors that contribute to their turnover. The aim of this study was to present a conceptual model based on predictors of nurses' turnover intention. Methods: A quantitative, non-experimental research design was used. A total of 308 registered nurses (RNs) took part in the study. The Multidimensional Work Motivation Scale (MWMS) and Practice Environment Scale of the Nursing Work Index (PES-NWI) were used. Results: The conceptual model, based on the binary regression models, relies on two direct significant predictors and four indirect significant predictors of turnover intention. The direct predictors are job satisfaction (OR = 0.23) and absenteeism (OR = 2.5). Indirect predictors that affect turnover intention via job satisfaction are: amotivation (OR = 0.59), identified regulation (OR = 0.54), intrinsic motivation (OR = 1.67), and nurse manager ability, leadership and support of nurses (OR = 1.51). Conclusions: The results of the study indicate strategic issues that need to be addressed to retain the nursing workforce. There is a need to ensure positive perceptions and support from managers, maintain intrinsic motivation, and promote even higher levels of motivation to achieve satisfactory levels of job satisfaction.
Keyphrases
  • healthcare
  • mental health
  • bone mineral density
  • primary care
  • public health
  • social support
  • mental illness
  • high resolution
  • social media
  • body composition
  • postmenopausal women
  • risk assessment