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Using the collaborative intervention planning framework to adapt a health-care manager intervention to a new population and provider group to improve the health of people with serious mental illness

Overview of attention for article published in Implementation Science, November 2014
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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 (78th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

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8 tweeters
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1 Facebook page

Citations

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39 Dimensions

Readers on

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116 Mendeley
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Title
Using the collaborative intervention planning framework to adapt a health-care manager intervention to a new population and provider group to improve the health of people with serious mental illness
Published in
Implementation Science, November 2014
DOI 10.1186/s13012-014-0178-9
Pubmed ID
Authors

Leopoldo J Cabassa, Arminda P Gomes, Quisqueya Meyreles, Lucia Capitelli, Richard Younge, Dianna Dragatsi, Juana Alvarez, Yamira Manrique, Roberto Lewis-Fernández

Abstract

BackgroundHealth-care manager interventions improve the physical health of people with serious mental illness (SMI) and could be widely implemented in public mental health clinics. Local adaptations and customization may be needed to increase the reach of these interventions in the public mental health system and across different racial and ethnic communities. In this study, we describe how we used the collaborative intervention planning framework to customize an existing health-care manager intervention to a new patient population (Hispanics with SMI) and provider group (social workers) to increase its fit with our local community.MethodsThe study was conducted in partnership with a public mental health clinic that serves predominantly Hispanic clients. A community advisory board (CAB) composed of researchers and potential implementers (e.g., social workers, primary care physicians) used the collaborative intervention planning framework, an approach that combines community-based participatory research principles and intervention mapping (IM) procedures, to inform intervention adaptations.ResultsThe adaptation process included four steps: fostering collaborations between CAB members; understanding the needs of the local population through a mixed-methods needs assessment, literature reviews, and group discussions; reviewing intervention objectives to identify targets for adaptation; and developing the adapted intervention. The application of this approach enabled the CAB to identify a series of cultural and provider level-adaptations without compromising the core elements of the original health-care manager intervention.ConclusionsReducing health disparities in people with SMI requires community engagement, particularly when preparing existing interventions to be used with new communities, provider groups, and practice settings. Our study illustrates one approach that can be used to involve community stakeholders in the intervention adaptation process from the very beginning to enhance the transportability of a health-care manager intervention in order to improve the health of people with SMI.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 115 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 21%
Researcher 16 14%
Student > Master 16 14%
Student > Bachelor 9 8%
Librarian 7 6%
Other 22 19%
Unknown 22 19%
Readers by discipline Count As %
Social Sciences 21 18%
Nursing and Health Professions 20 17%
Medicine and Dentistry 19 16%
Psychology 15 13%
Economics, Econometrics and Finance 2 2%
Other 9 8%
Unknown 30 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 15 December 2020.
All research outputs
#4,860,432
of 19,762,584 outputs
Outputs from Implementation Science
#950
of 1,643 outputs
Outputs of similar age
#72,328
of 335,409 outputs
Outputs of similar age from Implementation Science
#67
of 152 outputs
Altmetric has tracked 19,762,584 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,643 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.5. This one is in the 41st percentile – i.e., 41% 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 335,409 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 78% of its contemporaries.
We're also able to compare this research output to 152 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 55% of its contemporaries.