The researchers then attached two quality scores, sensitivity and predictive value positive with each GP dataset. While this is a valid approach to ascertaining the accuracy of a dataset, it also requires names and addresses to be present in multiple databases, a difficult proposition in a restrictive data access environment. Moreover, researchers are often interested in the quality of a dataset insofar as it affects the outcome of their analyses. Aims and objectives Health researchers across jurisdictions are interested in investigating the relationship of GP access and availability to various health outcomes [ 33 , 34 ]. While there are a number of approaches to quantifying GP availability, GP density in a geographical area is a commonly used metric [ 33 , 34 ]. In Australia GP densities by geography have been used as a metric of GP demand and supply [ 22 ]. A relevant research question in this context is whether the choice of one GP dataset over another affects the results of an analysis. If the same outcome were being studied, this would be equivalent to studying the level of agreement between the various datasets. The aim of the analysis presented in this paper thus, is to explore how the various GP datasets in Australia compare across different geographies. More specifically, we are interested in evaluating the correlation of GP headcounts and total FTE/FWE GPs at different geographic scales, and in observing how these correlations vary with rurality or remoteness. We also compare total headcounts and FTEs/FWEs from the various datasets across states and territories. This is intended to be an exploratory analysis of GP datasets, and it is anticipated that the results of our analyses will assist health services researchers in Australia to make informed choices about GP datasets.
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