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From Classification to Causality: Advancing Understanding of Mechanisms of Change in Implementation Science

Overview of attention for article published in Frontiers in Public Health, May 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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81 X users
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1 Google+ user

Citations

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367 Dimensions

Readers on

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345 Mendeley
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Title
From Classification to Causality: Advancing Understanding of Mechanisms of Change in Implementation Science
Published in
Frontiers in Public Health, May 2018
DOI 10.3389/fpubh.2018.00136
Pubmed ID
Authors

Cara C. Lewis, Predrag Klasnja, Byron J. Powell, Aaron R. Lyon, Leah Tuzzio, Salene Jones, Callie Walsh-Bailey, Bryan Weiner

Abstract

The science of implementation has offered little toward understanding how different implementation strategies work. To improve outcomes of implementation efforts, the field needs precise, testable theories that describe the causal pathways through which implementation strategies function. In this perspective piece, we describe a four-step approach to developing causal pathway models for implementation strategies. First, it is important to ensure that implementation strategies are appropriately specified. Some strategies in published compilations are well defined but may not be specified in terms of its core component that can have a reliable and measureable impact. Second, linkages between strategies and mechanisms need to be generated. Existing compilations do not offer mechanisms by which strategies act, or the processes or events through which an implementation strategy operates to affect desired implementation outcomes. Third, it is critical to identify proximal and distal outcomes the strategy is theorized to impact, with the former being direct, measurable products of the strategy and the latter being one of eight implementation outcomes (1). Finally, articulating effect modifiers, like preconditions and moderators, allow for an understanding of where, when, and why strategies have an effect on outcomes of interest. We argue for greater precision in use of terms for factors implicated in implementation processes; development of guidelines for selecting research design and study plans that account for practical constructs and allow for the study of mechanisms; psychometrically strong and pragmatic measures of mechanisms; and more robust curation of evidence for knowledge transfer and use.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 345 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 345 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 58 17%
Student > Ph. D. Student 55 16%
Student > Doctoral Student 35 10%
Student > Master 30 9%
Other 19 6%
Other 44 13%
Unknown 104 30%
Readers by discipline Count As %
Medicine and Dentistry 54 16%
Social Sciences 45 13%
Psychology 42 12%
Nursing and Health Professions 34 10%
Computer Science 6 2%
Other 40 12%
Unknown 124 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 49. 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 November 2023.
All research outputs
#871,411
of 25,732,188 outputs
Outputs from Frontiers in Public Health
#460
of 14,397 outputs
Outputs of similar age
#18,927
of 342,503 outputs
Outputs of similar age from Frontiers in Public Health
#11
of 96 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,397 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has done particularly well, scoring higher than 96% 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 342,503 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 94% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.