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Utilization of the Behavior Change Wheel framework to develop a model to improve cardiometabolic screening for people with severe mental illness

Overview of attention for article published in Implementation Science, November 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

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14 tweeters

Citations

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29 Dimensions

Readers on

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192 Mendeley
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Title
Utilization of the Behavior Change Wheel framework to develop a model to improve cardiometabolic screening for people with severe mental illness
Published in
Implementation Science, November 2017
DOI 10.1186/s13012-017-0663-z
Pubmed ID
Authors

Christina Mangurian, Grace C. Niu, Dean Schillinger, John W. Newcomer, James Dilley, Margaret A. Handley

Abstract

Individuals with severe mental illness (e.g., schizophrenia, bipolar disorder) die 10-25 years earlier than the general population, primarily from premature cardiovascular disease (CVD). Contributing factors are complex, but include systemic-related factors of poorly integrated primary care and mental health services. Although evidence-based models exist for integrating mental health care into primary care settings, the evidence base for integrating medical care into specialty mental health settings is limited. Such models are referred to as "reverse" integration. In this paper, we describe the application of an implementation science framework in designing a model to improve CVD outcomes for individuals with severe mental illness (SMI) who receive services in a community mental health setting. Using principles from the theory of planned behavior, focus groups were conducted to understand stakeholder perspectives of barriers to CVD risk factor screening and treatment identify potential target behaviors. We then applied results to the overarching Behavior Change Wheel framework, a systematic and theory-driven approach that incorporates the COM-B model (capability, opportunity, motivation, and behavior), to build an intervention to improve CVD risk factor screening and treatment for people with SMI. Following a stepped approach from the Behavior Change Wheel framework, a model to deliver primary preventive care for people that use community mental health settings as their de facto health home was developed. The CRANIUM (cardiometabolic risk assessment and treatment through a novel integration model for underserved populations with mental illness) model focuses on engaging community psychiatrists to expand their scope of practice to become responsible for CVD risk, with significant clinical decision support. The CRANIUM model was designed by integrating behavioral change theory and implementation theory. CRANIUM is feasible to implement, is highly acceptable to, and targets provider behavior change, and is replicable and efficient for helping to integrate primary preventive care services in community mental health settings. CRANIUM can be scaled up to increase CVD preventive care delivery and ultimately improve health outcomes among people with SMI served within a public mental health care system.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 192 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 192 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 39 20%
Student > Ph. D. Student 27 14%
Researcher 22 11%
Student > Bachelor 19 10%
Student > Doctoral Student 14 7%
Other 25 13%
Unknown 46 24%
Readers by discipline Count As %
Psychology 32 17%
Nursing and Health Professions 30 16%
Medicine and Dentistry 28 15%
Social Sciences 16 8%
Neuroscience 5 3%
Other 26 14%
Unknown 55 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 01 February 2018.
All research outputs
#2,058,301
of 12,448,635 outputs
Outputs from Implementation Science
#597
of 1,303 outputs
Outputs of similar age
#71,578
of 304,598 outputs
Outputs of similar age from Implementation Science
#33
of 43 outputs
Altmetric has tracked 12,448,635 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,303 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.9. This one has gotten more attention than average, scoring higher than 54% 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 304,598 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 76% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.