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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems
|
|---|---|
| Published in |
Implementation Science, March 2015
|
| DOI | 10.1186/s13012-015-0221-5 |
| Pubmed ID | |
| Authors |
Jo-An Atkinson, Andrew Page, Robert Wells, Andrew Milat, Andrew Wilson |
| Abstract |
In the design of public health policy, a broader understanding of risk factors for disease across the life course, and an increasing awareness of the social determinants of health, has led to the development of more comprehensive, cross-sectoral strategies to tackle complex problems. However, comprehensive strategies may not represent the most efficient or effective approach to reducing disease burden at the population level. Rather, they may act to spread finite resources less intensively over a greater number of programs and initiatives, diluting the potential impact of the investment. While analytic tools are available that use research evidence to help identify and prioritise disease risk factors for public health action, they are inadequate to support more targeted and effective policy responses for complex public health problems. This paper discusses the limitations of analytic tools that are commonly used to support evidence-informed policy decisions for complex problems. It proposes an alternative policy analysis tool which can integrate diverse evidence sources and provide a platform for virtual testing of policy alternatives in order to design solutions that are efficient, effective, and equitable. The case of suicide prevention in Australia is presented to demonstrate the limitations of current tools to adequately inform prevention policy and discusses the utility of the new policy analysis tool. In contrast to popular belief, a systems approach takes a step beyond comprehensive thinking and seeks to identify where best to target public health action and resources for optimal impact. It is concerned primarily with what can be reasonably left out of strategies for prevention and can be used to explore where disinvestment may occur without adversely affecting population health (or equity). Simulation modelling used for policy analysis offers promise in being able to better operationalise research evidence to support decision making for complex problems, improve targeting of public health policy, and offers a foundation for strengthening relationships between policy makers, stakeholders, and researchers. |
X Demographics
The data shown below were collected from the profiles of 23 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 17% |
| United Kingdom | 3 | 13% |
| Australia | 1 | 4% |
| Switzerland | 1 | 4% |
| Colombia | 1 | 4% |
| Norway | 1 | 4% |
| Unknown | 12 | 52% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 61% |
| Practitioners (doctors, other healthcare professionals) | 5 | 22% |
| Scientists | 3 | 13% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 293 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 2 | <1% |
| United Kingdom | 2 | <1% |
| Switzerland | 1 | <1% |
| Australia | 1 | <1% |
| Chile | 1 | <1% |
| Canada | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 284 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 58 | 20% |
| Student > Ph. D. Student | 44 | 15% |
| Student > Master | 40 | 14% |
| Student > Bachelor | 16 | 5% |
| Other | 15 | 5% |
| Other | 58 | 20% |
| Unknown | 62 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 55 | 19% |
| Medicine and Dentistry | 49 | 17% |
| Psychology | 27 | 9% |
| Nursing and Health Professions | 20 | 7% |
| Business, Management and Accounting | 11 | 4% |
| Other | 60 | 20% |
| Unknown | 71 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 11 May 2020.
All research outputs
#2,518,899
of 25,311,095 outputs
Outputs from Implementation Science
#520
of 1,798 outputs
Outputs of similar age
#30,332
of 263,731 outputs
Outputs of similar age from Implementation Science
#13
of 39 outputs
Altmetric has tracked 25,311,095 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,798 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has gotten more attention than average, scoring higher than 71% 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 263,731 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 88% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.