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Measuring rural allied health workforce turnover and retention: what are the patterns, determinants and costs?
Marita Chisholm1, Deborah Russell, John Humphreys
1School of Rural Health, Faculty of Medicine, Nursing and Health Sciences, Monash University, Bendigo, Victoria 3552, Australia.
Objectives:
To measure variations in patterns of turnover and retention, determinants of turnover, and costs of recruitment of allied health professionals in rural areas.
Design:
Data were collected on health service characteristics, recruitment costs and de-identified individual-level employment entry and exit data for dietitians, occupational therapists, physiotherapists, podiatrists, psychologists, social workers and speech pathologists employed between 1 January 2004 and 31 December 2009.
Setting:
Health services providing allied health services within Western Victoria were stratified by geographical location and town size. Eighteen health services were sampled, 11 participated.
Main Outcome Measures:
Annual turnover rates, stability rates, median length of stay in current position, survival probabilities, turnover hazards and median costs of recruitment were calculated.
Results:
Analysis of commencement and exit data from 901 allied health professionals indicated that differences in crude workforce patterns according to geographical location emerge 12 to 24 months after commencement of employment, although the results were not statistically significant. Proportional hazards modelling indicated profession and employee age and grade upon commencement were significant determinants of turnover risk. Costs of replacing allied health workers are high.
Conclusions:
An opportunity for implementing comprehensive retention strategies exists in the first year of employment in rural and remote settings. Benchmarks to guide workforce retention strategies should take account of differences in patterns of allied health turnover and retention according to geographical location. Monitoring allied health workforce turnover and retention through analysis of routinely collected data to calculate selected indicators provides a stronger evidence base to underpin workforce planning by health services and regional authorities.
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