↓ Skip to main content

Predicting research use in a public health policy environment: results of a logistic regression analysis

Overview of attention for article published in Implementation Science, October 2014
Altmetric Badge

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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
2 blogs
twitter
7 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
79 Mendeley
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
Predicting research use in a public health policy environment: results of a logistic regression analysis
Published in
Implementation Science, October 2014
DOI 10.1186/s13012-014-0142-8
Pubmed ID
Authors

Pauline Zardo, Alex Collie

Abstract

BackgroundUse of research evidence in public health policy decision-making is affected by a range of contextual factors operating at the individual, organisational and external levels. Context-specific research is needed to target and tailor research translation intervention design and implementation to ensure that factors affecting research in a specific context are addressed. Whilst such research is increasing, there remain relatively few studies that have quantitatively assessed the factors that predict research use in specific public health policy environments.MethodA quantitative survey was designed and implemented within two public health policy agencies in the Australian state of Victoria. Binary logistic regression analyses were conducted on survey data provided by 372 participants. Univariate logistic regression analyses of 49 factors revealed 26 factors that significantly predicted research use independently. The 26 factors were then tested in a single model and five factors emerged as significant predictors of research over and above all other factors.ResultsThe five key factors that significantly predicted research use were the following: relevance of research to day-to-day decision-making, skills for research use, internal prompts for use of research, intention to use research within the next 12 months and the agency for which the individual worked.ConclusionsThese findings suggest that individual- and organisational-level factors are the critical factors to target in the design of interventions aiming to increase research use in this context. In particular, relevance of research and skills for research use would be necessary to target. The likelihood for research use increased 11- and 4-fold for those who rated highly on these factors. This study builds on previous research and contributes to the currently limited number of quantitative studies that examine use of research evidence in a large sample of public health policy and program decision-makers within a specific context. The survey used in this study is likely to be relevant for use in other public health policy contexts.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
New Zealand 1 1%
Malaysia 1 1%
Unknown 75 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 22%
Researcher 14 18%
Student > Master 10 13%
Other 7 9%
Student > Bachelor 4 5%
Other 16 20%
Unknown 11 14%
Readers by discipline Count As %
Medicine and Dentistry 21 27%
Social Sciences 15 19%
Psychology 8 10%
Nursing and Health Professions 6 8%
Computer Science 4 5%
Other 10 13%
Unknown 15 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 03 February 2016.
All research outputs
#1,510,423
of 22,765,347 outputs
Outputs from Implementation Science
#319
of 1,721 outputs
Outputs of similar age
#18,315
of 255,208 outputs
Outputs of similar age from Implementation Science
#5
of 59 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one has done well, scoring higher than 81% 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 255,208 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.