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A conceptual model of treatment burden and patient capacity in stroke

Overview of attention for article published in BMC Primary Care, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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Title
A conceptual model of treatment burden and patient capacity in stroke
Published in
BMC Primary Care, January 2018
DOI 10.1186/s12875-017-0691-4
Pubmed ID
Authors

Katie I. Gallacher, Carl R. May, Peter Langhorne, Frances S. Mair

Abstract

Treatment burden is the workload of healthcare experienced by those with long-term conditions and the impact that this has on well-being. Treatment burden can negatively impact on quality of life and adherence to treatments. Individuals are likely to differ in their ability to manage health problems and follow treatments, defined as patient capacity. This has been under investigated in stroke. The aim of this paper is to create a conceptual model of treatment burden and patient capacity for people who have had a stroke through exploration of their experiences of healthcare. Interviews were conducted at home with 29 individuals who have had a stroke. These were recorded and transcribed verbatim. Fifteen explored treatment burden and were analysed by framework analysis underpinned by Normalisation Process Theory (NPT). Fourteen explored patient capacity and were analysed by thematic analysis. Taxonomies of treatment burden and patient capacity were created and a conceptual model produced. Mean age was 68 years. Sixteen were men and 13 women. The following broad areas of treatment burden were identified: making sense of stroke management and planning care; interacting with others including health professionals, family and other stroke patients; enacting management strategies; and reflecting on management. Treatment burdens were identified as arising from either: the workload of healthcare; or the endurance of care deficiencies. Six factors were identified that influence patient capacity: personal attributes and skills; physical and cognitive abilities; support network; financial status; life workload, and environment. Healthcare workload and the presence of care deficiencies can influence and be influenced by patient capacity. The quality and configuration of health and social care services has considerable influence on treatment burden and patient capacity. Findings have important implications for the design of clinical guidelines and healthcare delivery, highlighting issues such as the importance of good care co-ordination.

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The data shown below were collected from the profiles of 25 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 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 157 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 15%
Student > Ph. D. Student 19 12%
Student > Bachelor 16 10%
Researcher 15 10%
Student > Doctoral Student 6 4%
Other 20 13%
Unknown 57 36%
Readers by discipline Count As %
Medicine and Dentistry 24 15%
Nursing and Health Professions 24 15%
Social Sciences 13 8%
Psychology 7 4%
Agricultural and Biological Sciences 3 2%
Other 21 13%
Unknown 65 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 17 April 2020.
All research outputs
#2,746,886
of 25,382,440 outputs
Outputs from BMC Primary Care
#339
of 2,359 outputs
Outputs of similar age
#60,433
of 450,867 outputs
Outputs of similar age from BMC Primary Care
#9
of 50 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,359 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done well, scoring higher than 85% 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 450,867 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 86% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.