↓ Skip to main content

Barriers and opportunities for evidence-based health service planning: the example of developing a Decision Analytic Model to plan services for sexually transmitted infections in the UK

Overview of attention for article published in BMC Health Services Research, July 2012
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
55 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Barriers and opportunities for evidence-based health service planning: the example of developing a Decision Analytic Model to plan services for sexually transmitted infections in the UK
Published in
BMC Health Services Research, July 2012
DOI 10.1186/1472-6963-12-202
Pubmed ID
Authors

Catherine R H Aicken, Nigel T Armstrong, Jackie A Cassell, Neil Macdonald, Angela C Bailey, Sandra A Johnson, Catherine H Mercer

Abstract

Decision Analytic Models (DAMs) are established means of evidence-synthesis to differentiate between health interventions. They have mainly been used to inform clinical decisions and health technology assessment at the national level, yet could also inform local health service planning. For this, a DAM must take into account the needs of the local population, but also the needs of those planning its services. Drawing on our experiences from stakeholder consultations, where we presented the potential utility of a DAM for planning local health services for sexually transmitted infections (STIs) in the UK, and the evidence it could use to inform decisions regarding different combinations of service provision, in terms of their costs, cost-effectiveness, and public health outcomes, we discuss the barriers perceived by stakeholders to the use of DAMs to inform service planning for local populations, including (1) a tension between individual and population perspectives; (2) reductionism; and (3) a lack of transparency regarding models, their assumptions, and the motivations of those generating models.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 20%
Researcher 7 13%
Student > Ph. D. Student 5 9%
Librarian 3 5%
Student > Doctoral Student 3 5%
Other 15 27%
Unknown 11 20%
Readers by discipline Count As %
Medicine and Dentistry 13 24%
Business, Management and Accounting 5 9%
Nursing and Health Professions 4 7%
Social Sciences 4 7%
Economics, Econometrics and Finance 2 4%
Other 13 24%
Unknown 14 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 January 2013.
All research outputs
#13,669,218
of 23,305,591 outputs
Outputs from BMC Health Services Research
#4,693
of 7,800 outputs
Outputs of similar age
#79,997
of 144,359 outputs
Outputs of similar age from BMC Health Services Research
#78
of 122 outputs
Altmetric has tracked 23,305,591 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,800 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 144,359 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.