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Dynamics behind the scale up of evidence-based obesity prevention: protocol for a multi-site case study of an electronic implementation monitoring system in health promotion practice

Overview of attention for article published in Implementation Science, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

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Title
Dynamics behind the scale up of evidence-based obesity prevention: protocol for a multi-site case study of an electronic implementation monitoring system in health promotion practice
Published in
Implementation Science, December 2017
DOI 10.1186/s13012-017-0686-5
Pubmed ID
Authors

Kathleen P. Conte, Sisse Groen, Victoria Loblay, Amanda Green, Andrew Milat, Lina Persson, Christine Innes-Hughes, Jo Mitchell, Sarah Thackway, Mandy Williams, Penelope Hawe

Abstract

The effectiveness of many interventions to promote health and prevent disease has been well established. The imperative has therefore shifted from amassing evidence about efficacy to scale-up to maximise population-level health gains. Electronic implementation monitoring, or 'e-monitoring', systems have been designed to assist and track the delivery of preventive policies and programs. However, there is little evidence on whether e-monitoring systems improve the dissemination, adoption, and ongoing delivery of evidence-based preventive programs. Also, given considerable difficulties with e-monitoring systems in the clinical sector, scholars have called for a more sophisticated re-examination of e-monitoring's role in enhancing implementation. In the state of New South Wales (NSW), Australia, the Population Health Information Management System (PHIMS) was created to support the dissemination of obesity prevention programs to 6000 childcare centres and elementary schools across all 15 local health districts. We have established a three-way university-policymaker-practice research partnership to investigate the impact of PHIMS on practice, how PHIMS is used, and how achievement of key performance indicators of program adoption may be associated with local contextual factors. Our methods encompass ethnographic observation, key informant interviews and participatory workshops for data interpretation at a state and local level. We use an on-line social network analysis of the collaborative relationships across local health district health promotion teams to explore the relationship between PHIMS use and the organisational structure of practice. Insights will be sensitised by institutional theory, practice theory and complex adaptive system thinking, among other theories which make sense of socio-technical action. Our working hypothesis is that the science of getting evidence-based programs into practice rests on an in-depth understanding of the role they play in the on-going system of local relationships and multiple accountabilities. Data will be synthesised to produce a typology to characterise local context, PHIMS use and key performance indicator achievement (of program implementation) across the 15 local health districts. Results could be used to continuously align e-monitoring technologies within quality improvement processes to ensure that such technologies enhance practice and innovation. A partnership approach to knowledge production increases the likelihood that findings will be put into practice.

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The data shown below were collected from the profiles of 11 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 148 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 16%
Researcher 18 12%
Student > Master 18 12%
Student > Bachelor 14 9%
Other 10 7%
Other 24 16%
Unknown 41 28%
Readers by discipline Count As %
Medicine and Dentistry 23 16%
Nursing and Health Professions 21 14%
Social Sciences 19 13%
Psychology 9 6%
Engineering 8 5%
Other 21 14%
Unknown 47 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 10 December 2017.
All research outputs
#4,715,418
of 23,009,818 outputs
Outputs from Implementation Science
#920
of 1,723 outputs
Outputs of similar age
#101,364
of 439,982 outputs
Outputs of similar age from Implementation Science
#31
of 40 outputs
Altmetric has tracked 23,009,818 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,723 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one is in the 46th percentile – i.e., 46% 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 439,982 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 76% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.