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Proof of impact and pipeline planning: directions and challenges for social audit in the health sector

Overview of attention for article published in BMC Health Services Research, December 2011
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Title
Proof of impact and pipeline planning: directions and challenges for social audit in the health sector
Published in
BMC Health Services Research, December 2011
DOI 10.1186/1472-6963-11-s2-s16
Pubmed ID
Authors

Neil Andersson

Abstract

Social audits are typically observational studies, combining qualitative and quantitative uptake of evidence with consultative interpretation of results. This often falters on issues of causality because their cross-sectional design limits interpretation of time relations and separation out of other indirect associations.Social audits drawing on methods of randomised controlled cluster trials (RCCT) allow more certainty about causality. Randomisation means that exposure occurs independently of all events that precede it--it converts potential confounders and other covariates into random differences. In 2008, CIET social audits introduced randomisation of the knowledge translation component with subsequent measurement of impact in the changes introduced. This "proof of impact" generates an additional layer of evidence in a cost-effective way, providing implementation-ready solutions for planners.Pipeline planning is a social audit that incorporates stepped wedge RCCTs. From a listing of districts/communities as a sampling frame, individual entities (communities, towns, districts) are randomly assigned to waves of intervention. Measurement of the impact takes advantage of the delay occasioned by the reality that there are insufficient resources to implement everywhere at the same time. The impact in the first wave contrasts with the second wave, which in turn contrasts with a third wave, and so on until all have received the intervention. Provided care is taken to achieve reasonable balance in the random allocation of communities, towns or districts to the waves, the resulting analysis can be straightforward.Where there is sufficient management interest in and commitment to evidence, pipeline planning can be integrated in the roll-out of programmes where real time information can improve the pipeline. Not all interventions can be randomly allocated, however, and random differences can still distort measurement. Other issues include contamination of the subsequent waves, ambiguity of indicators, "participant effects" that result from lack of blinding and lack of placebos, ethics and, not least important, the skills to do pipeline planning correctly.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Student > Master 8 18%
Student > Doctoral Student 5 11%
Researcher 4 9%
Student > Postgraduate 3 7%
Other 6 13%
Unknown 10 22%
Readers by discipline Count As %
Medicine and Dentistry 13 29%
Social Sciences 6 13%
Nursing and Health Professions 4 9%
Agricultural and Biological Sciences 2 4%
Environmental Science 2 4%
Other 6 13%
Unknown 12 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 March 2013.
All research outputs
#20,187,333
of 22,703,044 outputs
Outputs from BMC Health Services Research
#7,066
of 7,592 outputs
Outputs of similar age
#220,434
of 243,149 outputs
Outputs of similar age from BMC Health Services Research
#66
of 74 outputs
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We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.