OBJECTIVE: This article identifies, critically appraises and illustrates the use of five key workforce turnover and retention metrics that are well suited for use by Australian rural health workforce planners. These are crude turnover (separation) rates, stability rates, survival probabilities, median survival and Cox proportional hazard ratios. Examples of their calculation are presented using actual data obtained from payroll records in Australian rural and remote health services. CONCLUSION: The use of this small number of metrics as a 'workforce measurement package' can help overcome many of the limitations evident when a single measure is reported in isolation, by providing a more comprehensive picture of turnover and retention patterns. We suggest that health services themselves can calculate the simplest measures, whereas regional and centralised health authorities with higher levels of expertise undertake survival analysis and comparisons of compiled data. IMPLICATIONS: These key metrics can be used routinely to measure baseline levels of health worker turnover and retention, to quantify important determinants of turnover and retention, and importantly, to make valid comparisons. This enables areas for improvement to be better targeted using appropriate retention strategies, and changes resulting from retention interventions to be evaluated effectively.
OBJECTIVE: This article identifies, critically appraises and illustrates the use of five key workforce turnover and retention metrics that are well suited for use by Australian rural health workforce planners. These are crude turnover (separation) rates, stability rates, survival probabilities, median survival and Cox proportional hazard ratios. Examples of their calculation are presented using actual data obtained from payroll records in Australian rural and remote health services. CONCLUSION: The use of this small number of metrics as a 'workforce measurement package' can help overcome many of the limitations evident when a single measure is reported in isolation, by providing a more comprehensive picture of turnover and retention patterns. We suggest that health services themselves can calculate the simplest measures, whereas regional and centralised health authorities with higher levels of expertise undertake survival analysis and comparisons of compiled data. IMPLICATIONS: These key metrics can be used routinely to measure baseline levels of health worker turnover and retention, to quantify important determinants of turnover and retention, and importantly, to make valid comparisons. This enables areas for improvement to be better targeted using appropriate retention strategies, and changes resulting from retention interventions to be evaluated effectively.
Authors: Cristian Garcia-Alcaraz; Scott C Roesch; Elizabeth Calhoun; Patrick Wightman; Prashanthinie Mohan; Tracy A Battaglia; Rosa Cobian Aguilar; Patricia A Valverde; Kristen J Wells Journal: Cancer Date: 2022-07-01 Impact factor: 6.921
Authors: Deborah J Russell; Yuejen Zhao; Steven Guthridge; Mark Ramjan; Michael P Jones; John S Humphreys; John Wakerman Journal: Hum Resour Health Date: 2017-08-15
Authors: John Wakerman; John Humphreys; Lisa Bourke; Terry Dunbar; Michael Jones; Timothy A Carey; Steven Guthridge; Deborah Russell; David Lyle; Yuejen Zhao; Lorna Murakami-Gold Journal: JMIR Res Protoc Date: 2016-10-03