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Toward optimal implementation of cancer prevention and control programs in public health: a study protocol on mis-implementation

Overview of attention for article published in Implementation Science, March 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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13 X users
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1 Google+ user

Citations

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22 Dimensions

Readers on

mendeley
87 Mendeley
citeulike
1 CiteULike
Title
Toward optimal implementation of cancer prevention and control programs in public health: a study protocol on mis-implementation
Published in
Implementation Science, March 2018
DOI 10.1186/s13012-018-0742-9
Pubmed ID
Authors

Margaret Padek, Peg Allen, Paul C. Erwin, Melissa Franco, Ross A. Hammond, Benjamin Heuberger, Matt Kasman, Doug A. Luke, Stephanie Mazzucca, Sarah Moreland-Russell, Ross C. Brownson

Abstract

Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 87 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 14%
Researcher 10 11%
Student > Ph. D. Student 10 11%
Student > Doctoral Student 7 8%
Other 5 6%
Other 14 16%
Unknown 29 33%
Readers by discipline Count As %
Medicine and Dentistry 11 13%
Social Sciences 11 13%
Nursing and Health Professions 6 7%
Psychology 6 7%
Engineering 5 6%
Other 13 15%
Unknown 35 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 24 October 2019.
All research outputs
#3,268,291
of 23,313,051 outputs
Outputs from Implementation Science
#700
of 1,728 outputs
Outputs of similar age
#68,828
of 332,233 outputs
Outputs of similar age from Implementation Science
#25
of 42 outputs
Altmetric has tracked 23,313,051 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,728 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one has gotten more attention than average, scoring higher than 59% 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 332,233 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 79% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.