Utilising a collective case study system theory mixed methods approach: a rural health example

BACKGROUND: Insight into local health service provision in rural communities is limited in the literature. The dominant workforce focus in the rural health literature, while revealing issues of shortage of maldistribution, does not describe service provision in rural towns. Similarly aggregation of data tends to render local health service provision virtually invisible. This paper describes a methodology to explore specific aspects of rural health service provision with an initial focus on understanding rurality as it pertains to rural physiotherapy service provision. METHOD: A system theory-case study heuristic combined with a sequential mixed methods approach to provide a framework for both quantitative and qualitative exploration across sites. Stakeholder perspectives were obtained through surveys and in depth interviews. The investigation site was a large area of one Australian state with a mix of rural, regional and remote communities. RESULTS: 39 surveys were received from 11 locations within the investigation site and 19 in depth interviews were conducted. Stakeholder perspectives of rurality and workforce numbers informed the development of six case types relevant to the exploration of rural physiotherapy service provision. Participant perspective of rurality often differed with the geographical classification of their location. The numbers of onsite colleagues and local access to health services contributed to participant perceptions of rurality. CONCLUSIONS: The complexity of understanding the concept of rurality was revealed by interview participants when providing their perspectives about rural physiotherapy service provision. Dual measures, such as rurality and workforce numbers, provide more relevant differentiation of sites to explore specific services, such rural physiotherapy service provision, than single measure of rurality as defined by geographic classification. The system theory-case study heuristic supports both qualitative and quantitative exploration in rural health services research.