{"title":"A Statistical Analysis of the Staff Data to Evaluate the Influence of the Retention Factors in the NHS England","authors":"Sharif Ahmed, M. A. Hossain, Zia Ush-Shamszaman","doi":"10.1109/SKIMA57145.2022.10029576","DOIUrl":null,"url":null,"abstract":"The National Health Service (NHS) has 1.3 million staff who care for the people of England with skill, compassion, and dedication. Between 1 January 2020 - 31 March 2020, there were 84,393 advertised vacancy full-time equivalents in NHS England. Among them, around 40% is Nursing and Midwifery Registered Staff Group. To tackle the staff shortage, NHS needs to spend about £480m per year on temporary staff. This paper presents an investigation of the three complementary methods to analyse retention data: (i) one that explores comparisons for engagement and retention within a year, (ii) differences across organisations in any observable or unobservable predictors and finally, (iii) the focuses on changes within NHS trusts across several years. Generally, a higher level of staff engagement indicates a better retention rate; but the findings of this investigation demonstrate a reverse result. Highly likely unmeasured external factors may influence the outputs, and possible factors may be organisation restructure, local labour market variations and more subtle between trust types. The latent Growth Curve model statistical technique is used to estimate growth trajectories. Using this technique, we estimated the change over time in staff retention. It shows a clear positive link between changes in engagement and changes in retention. Results show a 1% increase in the initial year of involvement (2015 involvement) is associated with a rise of 3.0 per cent in retention rate each year in the following periods.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"38 11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SKIMA57145.2022.10029576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The National Health Service (NHS) has 1.3 million staff who care for the people of England with skill, compassion, and dedication. Between 1 January 2020 - 31 March 2020, there were 84,393 advertised vacancy full-time equivalents in NHS England. Among them, around 40% is Nursing and Midwifery Registered Staff Group. To tackle the staff shortage, NHS needs to spend about £480m per year on temporary staff. This paper presents an investigation of the three complementary methods to analyse retention data: (i) one that explores comparisons for engagement and retention within a year, (ii) differences across organisations in any observable or unobservable predictors and finally, (iii) the focuses on changes within NHS trusts across several years. Generally, a higher level of staff engagement indicates a better retention rate; but the findings of this investigation demonstrate a reverse result. Highly likely unmeasured external factors may influence the outputs, and possible factors may be organisation restructure, local labour market variations and more subtle between trust types. The latent Growth Curve model statistical technique is used to estimate growth trajectories. Using this technique, we estimated the change over time in staff retention. It shows a clear positive link between changes in engagement and changes in retention. Results show a 1% increase in the initial year of involvement (2015 involvement) is associated with a rise of 3.0 per cent in retention rate each year in the following periods.