Commentary: Does registered nurse involvement in improving healthcare services, influence registered nurse retention?

Secondary research and analysis of existing data is an innovative and efficient way of using published health service data, reports and documents and has the potential to shine a light into areas of health services that may not otherwise get any research attention. The quality of secondary health-related research and analysis relies entirely on the health services, their associated agencies, and researchers making the data collected in original research studies and in their health-related systems available to qualified researchers who were not involved in the primary research or in the creation and maintenance of the data systems. Secondary health service related research and analysis is more cost-effective than primary research, as it makes use of already existing health service data, unlike primary health service research where data are collected first hand by researchers; sometimes at great costs to the public purse (Nutley et al., 2007). This paper analyses and correlates published data from the NHS Staff Survey 2018 and data from the 2019 Model Health System regarding RN involvement in improvement and RN retention.

What is particularly novel about this paper is the use of a non-parametric alternative (Kendall’s tau-b) to measure the strength and direction of associations that exists between the two variables RN retention and NHS improvement. Non-parametric correlation coefficient measures are usually applied if the data being analysed fail normality assumptions, i.e. are not normally distributed or when one has a small sample size with many tied ranks in the data. This study reported both a small sample size and that the data were not normally distributed. In my experience most nurse researchers involved in quantitative data analysis stick with the more common non-parametric measures such as Pearson’s correlation or Spearman’s correlation and it was therefore refreshing to read (and also research and understand) how there was an association between 2018 RN retention data and the three improvement questions included in the 2018 Model Health system questionnaire which were reported in 2019. Simple, but quite effective at demonstrating association.

Although this paper highlights the significant positive correlations between nurses' ‘ability to make suggestions to improve the work of teams/departments’ in NHS mental health services in England and the antithesis in relation to RNs in acute NHS services ‘making improvements happen’, the conclusion and implications for RN retention, retention strategies and indeed for quality improvement in the NHS are, in my opinion, relatively passive. It will come as no surprise to readers that improvement efforts in health service settings ‘engage’ almost all involved with them and in them, including RNs. This is cited by the authors through the work of Robinson and Gelling (2019) but is also well accepted in the literature through my own work (White et al., 2014, 2017a, 2017b) and the work of others e.g. Wee and Lai (2021). This paper will therefore serve as confirmatory evidence (instead of new evidence) in the growing body of literature supporting improvement and quality improvement as an important element of enhancing employee engagement in health services.

Finally, the authors eloquently draw attention to the limitations of secondary research and analysis by emphasising the need for further in-depth qualitative studies to understand what ‘involvement in improvement’ means to RNs and the relationship between RN involvement and improvement from the perspectives of RNs working in different contexts (mental health versus acute NHS settings in England). One limitation that could have been highlighted in the paper is how researchers involved in secondary analysis of health service data are probably unaware of the study-specific nuances or glitches within the primary data collection process and how that may influence (or not influence) the interpretation of specific variables in the dataset; and that the available data were not collected to address the particular research question or test the particular hypothesis which is now being examined via the lens of secondary data analysis.

Biography

A nurse and midwife, Mark White is the Vice President of Research, Innovation and Graduate Studies at Waterford Institute of Technology. He is serving a term as chair of the Irish Health Research Forum, is a fellow of the Faculty of Nursing and Midwifery in the Royal College of Surgeons in Ireland and as a member of the Board of Omega Epsilon At Large, Ireland’s only Chapter of Sigma Nursing. He is also a member of the Editorial Board of the Journal of Research in Nursing.