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The relationship between effective transition models and the optimal management of patient care

14 February 2019
Volume 28 · Issue 3


There is a shortfall of over 10% of the NHS workforce in the UK and the Nursing and Midwifery Council has reported that an estimated 18% of nurses leave the profession altogether within 3 years of qualification. Canada and the USA also face retention issues. The transition period from student to registered nurse is considered an important period during which the newly qualified nurse (NQN) gains experience. This article aims to analyse how the transition from student to registered nurse can be more effectively facilitated, thereby enhancing the delivery of patient care. It examines how NQNs perceive the transition period and discusses how effective transition supports the optimal management of patient care.

In recent years, many countries have faced serious nursing staff shortages, representing a major global healthcare challenge (Buerhaus et al, 2009; International Council of Nurses, 2019). The same problem has occurred in the UK (NHS Employers, 2015), where staff shortages mean that health professionals are put under increasing pressure to deliver the high quality care the public has a right to expect. In the UK alone, there is a shortfall of over 10% of the workforce and the Nursing and Midwifery Council (NMC) reported that more nurses are leaving the profession than are registering to practise: between 2016 and 2017, 27% more registrants left than joined (NMC, 2017).

Likewise, according to Laschinger and Read (2016), in Canada nearly 60% of nurses change or leave the profession within 2 years of employment and 25% of US nurses leave a position within the first year of practice (Spector, 2015). While the main causes of nurses leaving the profession are unknown, it has been shown that poor nursing environments, lack of mentoring and ‘reality shock’ (Kumaran and Carney, 2014) can often contribute to snowballing attrition rates (Flinkman and Salanterä, 2015; Kenny et al, 2016; Lo et al, 2018).

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