Person-centred analysis of first-year rural nurses' work attitudes and intentions

Background<br/><br/>Organisational behaviour research has identified that autonomously motivated workers have better occupational well-being and retention. In the rural health literature, it is well-established that those with greater place attachment, such as locals with strong social networks in the area, are more likely to be recruited and retained in the rural health workforce. It is unclear how these two major components of retention function among Australian rural health professionals and relate to their rural work attitudes and intentions.<br/><br/>Informed by self-determination theory as well as place-based and whole-of-person approaches, this analysis explored the utility of a quantitative person-centred approach to identify subgroups (“classes”) of commencing first-year rural nurses and examine their work attitudes and intentions.<br/><br/>Methods<br/><br/>A cross-sectional survey of commencing first-year nurses was conducted in two Australian rural health districts, with a 91% response rate. A two-step latent class analysis of 159 responses was undertaken with two indicators of perceived professional opportunity and three indicators of local connection and rural training. Three outcomes assessing work attitudes (occupational happiness and attraction to staying and working rurally at first-year site) and future rural work intentions were compared across subtypes.<br/><br/>Results<br/><br/>Four classes of rural first-year nurses were identified. There were key differences between the classes on the assessed outcomes. Early career nurses who were rural-locals with highest perceptions of professional opportunities had the highest intention to remain working rurally and were happiest. Non-locals with high perceptions of opportunities were significantly happier than both rural-locals and non-locals with lower perceptions.<br/><br/>Conclusion<br/><br/>Identified subtypes of rural first-year nurses differed in work attitudes and future rural workforce intentions. Early career nurses who were rural-locals and perceived positive professional opportunities had the highest intention to remain working rurally. This research demonstrates how a quantitative person-centred approach can illuminate heterogeneous subgroups within a rural health workforce to inform more targeted recruitment and retention strategies. The process and utility of this method for use by rural health organisations and researchers is discussed.

Authors

Institutions

Publication Details

Journal
Charles Sturt University Research Output (CRO)
Published
2025-10-08
Primary Topic
Global Health Workforce Issues
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Person-centred analysis of first-year rural nurses' work attitudes and intentions

Claire Ellen; id_orcid 0000-0003-1565-4130 Seaman
Charles Sturt University Research Output (CRO)
Global Health Workforce Issues
article

Person-centred analysis of first-year rural nurses' work attitudes and intentions

Claire Ellen; id_orcid 0000-0003-1565-4130 Seaman
article en

Abstract

Background<br/><br/>Organisational behaviour research has identified that autonomously motivated workers have better occupational well-being and retention. In the rural health literature, it is well-established that those with greater place attachment, such as locals with strong social networks in the area, are more likely to be recruited and retained in the rural health workforce. It is unclear how these two major components of retention function among Australian rural health professionals and relate to their rural work attitudes and intentions.<br/><br/>Informed by self-determination theory as well as place-based and whole-of-person approaches, this analysis explored the utility of a quantitative person-centred approach to identify subgroups (“classes”) of commencing first-year rural nurses and examine their work attitudes and intentions.<br/><br/>Methods<br/><br/>A cross-sectional survey of commencing first-year nurses was conducted in two Australian rural health districts, with a 91% response rate. A two-step latent class analysis of 159 responses was undertaken with two indicators of perceived professional opportunity and three indicators of local connection and rural training. Three outcomes assessing work attitudes (occupational happiness and attraction to staying and working rurally at first-year site) and future rural work intentions were compared across subtypes.<br/><br/>Results<br/><br/>Four classes of rural first-year nurses were identified. There were key differences between the classes on the assessed outcomes. Early career nurses who were rural-locals with highest perceptions of professional opportunities had the highest intention to remain working rurally and were happiest. Non-locals with high perceptions of opportunities were significantly happier than both rural-locals and non-locals with lower perceptions.<br/><br/>Conclusion<br/><br/>Identified subtypes of rural first-year nurses differed in work attitudes and future rural workforce intentions. Early career nurses who were rural-locals and perceived positive professional opportunities had the highest intention to remain working rurally. This research demonstrates how a quantitative person-centred approach can illuminate heterogeneous subgroups within a rural health workforce to inform more targeted recruitment and retention strategies. The process and utility of this method for use by rural health organisations and researchers is discussed.

Charles Sturt University Research Output (CRO)
Three Rivers College (US)
Openalex Percentile: Top 19%
Global Health Workforce Issues
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.