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Improving the planning of the GP workforce in Australia: a simulation model incorporating work transitions, health need and service usage

Overview of attention for article published in Human Resources for Health, April 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

news
1 news outlet
twitter
4 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
46 Mendeley
Title
Improving the planning of the GP workforce in Australia: a simulation model incorporating work transitions, health need and service usage
Published in
Human Resources for Health, April 2016
DOI 10.1186/s12960-016-0110-2
Pubmed ID
Authors

Caroline O. Laurence, Jonathan Karnon

Abstract

In Australia, the approach to health workforce planning has been supply-led and resource-driven rather than need-based. The result has been cycles of shortages and oversupply. These approaches have tended to use age and sex projections as a measure of need or demand for health care. Less attention has been given to more complex aspects of the population, such as the increasing proportion of the ageing population and increasing levels of chronic diseases or changes in the mix of health care providers or their productivity levels. These are difficult measures to get right and so are often avoided. This study aims to develop a simulation model for planning the general practice workforce in South Australia that incorporates work transitions, health need and service usage. A simulation model was developed with two sub-models-a supply sub-model and a need sub-model. The supply sub-model comprised three components-training, supply and productivity-and the need sub-model described population size, health needs, service utilisation rates and productivity. A state transition cohort model is used to estimate the future supply of GPs, accounting for entries and exits from the workforce and changes in location and work status. In estimating the required number of GPs, the model used incidence and prevalence data, combined with age, gender and condition-specific utilisation rates. The model was run under alternative assumptions reflecting potential changes in need and utilisation rates over time. The supply sub-model estimated the number of full-time equivalent (FTE) GP stock in SA for the period 2004-2011 and was similar to the observed data, although it had a tendency to overestimate the GP stock. The three scenarios presented for the demand sub-model resulted in different outcomes for the estimated required number of GPs. For scenario one, where utilisation rates in 2003 were assumed optimal, the model predicted fewer FTE GPs were required than was observed. In scenario 2, where utilisation rates in 2013 were assumed optimal, the model matched observed data, and in scenario 3, which assumed increasing age- and gender-specific needs over time, the model predicted more FTE GPs were required than was observed. This study provides a robust methodology for determining supply and demand for one professional group at a state level. The supply sub-model was fitted to accurately represent workforce behaviours. In terms of demand, the scenario analysis showed variation in the estimations under different assumptions that demonstrates the value of monitoring population-based need over time. In the meantime, expert opinion might identify the most relevant scenario to be used in projecting workforce requirements.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 2%
Brazil 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 28%
Other 5 11%
Student > Ph. D. Student 5 11%
Researcher 5 11%
Student > Doctoral Student 4 9%
Other 8 17%
Unknown 6 13%
Readers by discipline Count As %
Medicine and Dentistry 12 26%
Nursing and Health Professions 8 17%
Engineering 4 9%
Economics, Econometrics and Finance 4 9%
Business, Management and Accounting 3 7%
Other 6 13%
Unknown 9 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 March 2019.
All research outputs
#1,458,033
of 14,120,633 outputs
Outputs from Human Resources for Health
#187
of 769 outputs
Outputs of similar age
#37,042
of 263,799 outputs
Outputs of similar age from Human Resources for Health
#2
of 8 outputs
Altmetric has tracked 14,120,633 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 769 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,799 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.