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General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare
BACKGROUND: Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766700/ https://www.ncbi.nlm.nih.gov/pubmed/24005003 http://dx.doi.org/10.1186/1472-6963-13-343 |
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author | Mazumdar, Soumya Konings, Paul Butler, Danielle McRae, Ian Stewart |
author_facet | Mazumdar, Soumya Konings, Paul Butler, Danielle McRae, Ian Stewart |
author_sort | Mazumdar, Soumya |
collection | PubMed |
description | BACKGROUND: Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere. Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness. METHOD: We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness. RESULTS: The datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets. CONCLUSION: In the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets. |
format | Online Article Text |
id | pubmed-3766700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37667002013-09-09 General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare Mazumdar, Soumya Konings, Paul Butler, Danielle McRae, Ian Stewart BMC Health Serv Res Research Article BACKGROUND: Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere. Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness. METHOD: We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness. RESULTS: The datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets. CONCLUSION: In the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets. BioMed Central 2013-09-03 /pmc/articles/PMC3766700/ /pubmed/24005003 http://dx.doi.org/10.1186/1472-6963-13-343 Text en Copyright © 2013 Mazumdar et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Mazumdar, Soumya Konings, Paul Butler, Danielle McRae, Ian Stewart General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare |
title | General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare |
title_full | General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare |
title_fullStr | General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare |
title_full_unstemmed | General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare |
title_short | General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare |
title_sort | general practitioner (family physician) workforce in australia: comparing geographic data from surveys, a mailing list and medicare |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766700/ https://www.ncbi.nlm.nih.gov/pubmed/24005003 http://dx.doi.org/10.1186/1472-6963-13-343 |
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