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Factors influencing the adoption of self-management solutions: an interpretive synthesis of the literature on stakeholder experiences

Overview of attention for article published in Implementation Science, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

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16 tweeters

Citations

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10 Dimensions

Readers on

mendeley
101 Mendeley
Title
Factors influencing the adoption of self-management solutions: an interpretive synthesis of the literature on stakeholder experiences
Published in
Implementation Science, November 2015
DOI 10.1186/s13012-015-0350-x
Pubmed ID
Authors

J. Harvey, S. Dopson, R. J. McManus, J. Powell

Abstract

In a research context, self-management solutions, which may range from simple book diaries to complex telehealth packages, designed to facilitate patients in managing their long-term conditions, have often shown cost-effectiveness, but their implementation in practice has frequently been challenging. We conducted an interpretive qualitative synthesis of relevant articles identified through systematic searches of bibliographic databases in July 2014. We searched PubMed (Medline/NLM), Web of Science, LISTA (EBSCO), CINAHL, Embase and PsycINFO. Coding and analysis was inductive, using the framework method to code and to categorise themes. We took a sensemaking approach to the interpretation of findings. Fifty-eight articles were selected for synthesis. Results showed that during adoption, factors identified as facilitators by some were experienced as barriers by others, and facilitators could change to barriers for the same adopter, depending on how adopters rationalise the solutions within their context when making decisions about (retaining) adoption. Sometimes, when adopters saw and experienced benefits of a solution, they continued using the solution but changed their minds when they could no longer see the benefits. Thus, adopters placed a positive value on the solution if they could constructively rationalise it (which increased adoption) and attached a negative rationale (decreasing adoption) if the solution did not meet their expectations. Key factors that influenced the way adopters rationalised the solutions consisted of costs and the added value of the solution to them and moral, social, motivational and cultural factors. Considering 'barriers' and 'facilitators' for implementation may be too simplistic. Implementers could instead iteratively re-evaluate how potential facilitators and barriers are being experienced by adopters throughout the implementation process, to help adopters to retain constructive evaluations of the solution. Implementers need to pay attention to factors including (a) cost: how much resource will the intervention cost the patient or professional; (b) moral: to what extent will people adhere because they want to be 'good' patients and professionals;

Twitter Demographics

The data shown below were collected from the profiles of 16 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 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Australia 1 <1%
United States 1 <1%
Unknown 97 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 22%
Researcher 22 22%
Student > Ph. D. Student 13 13%
Unspecified 11 11%
Student > Bachelor 8 8%
Other 24 24%
Unknown 1 <1%
Readers by discipline Count As %
Medicine and Dentistry 23 23%
Nursing and Health Professions 18 18%
Unspecified 14 14%
Psychology 12 12%
Social Sciences 11 11%
Other 22 22%
Unknown 1 <1%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 December 2015.
All research outputs
#2,047,673
of 13,349,286 outputs
Outputs from Implementation Science
#590
of 1,380 outputs
Outputs of similar age
#50,542
of 280,115 outputs
Outputs of similar age from Implementation Science
#81
of 170 outputs
Altmetric has tracked 13,349,286 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,380 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. This one has gotten more attention than average, scoring higher than 56% 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 280,115 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 81% of its contemporaries.
We're also able to compare this research output to 170 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 52% of its contemporaries.