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Multimorbidity: can general practitioners identify the health conditions most important to their patients? Results from a national cross-sectional study in Switzerland

Overview of attention for article published in BMC Family Practice, May 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

Mentioned by

twitter
7 tweeters

Citations

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2 Dimensions

Readers on

mendeley
14 Mendeley
Title
Multimorbidity: can general practitioners identify the health conditions most important to their patients? Results from a national cross-sectional study in Switzerland
Published in
BMC Family Practice, May 2018
DOI 10.1186/s12875-018-0757-y
Pubmed ID
Authors

Anouk Déruaz-Luyet, Alexandra A. N’Goran, Jérôme Pasquier, Bernard Burnand, Patrick Bodenmann, Stefan Zechmann, Stefan Neuner-Jehle, Nicolas Senn, Daniel Widmer, Sven Streit, Andreas Zeller, Dagmar M. Haller, Lilli Herzig

Abstract

Faced with patients suffering from more than one chronic condition, or multimorbidity, general practitioners (GPs) must establish diagnostic and treatment priorities. Patients also set their own priorities to handle the everyday burdens associated with their multimorbidity and these may be different from the priorities established by their GP. A shared patient-GP agenda, driven by knowledge of each other's priorities, would seem central to managing patients with multimorbidity. We evaluated GPs' ability to identify the health condition most important to their patients. Data on 888 patients were collected as part of a cross-sectional Swiss study on multimorbidity in family medicine. For the main analyses on patients-GP agreement, data from 572 of these patients could be included. GPs were asked to identify the two conditions which their patient considered most important, and we tested whether either of them agreed with the condition mentioned as most important by the patient. In the main analysis, we studied the agreement rate between GPs and patients by grouping items medically-related into 46 groups of conditions. Socio-demographic and clinical variables were fitted into univariate and multivariate models. In 54.9% of cases, GPs were able to identify the health condition most important to the patient. In the multivariate model, the only variable significantly associated with patient-GP agreement was the number of chronic conditions: the higher the number of conditions, the less likely the agreement. GPs were able to correctly identify the health condition most important to their patients in half of the cases. It therefore seems important that GPs learn how to better adapt treatment targets and priorities by taking patients' perspectives into account.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 14%
Student > Doctoral Student 2 14%
Student > Ph. D. Student 2 14%
Professor 1 7%
Student > Bachelor 1 7%
Other 3 21%
Unknown 3 21%
Readers by discipline Count As %
Medicine and Dentistry 5 36%
Social Sciences 2 14%
Nursing and Health Professions 2 14%
Unknown 5 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 23 August 2019.
All research outputs
#3,986,256
of 13,897,020 outputs
Outputs from BMC Family Practice
#559
of 1,396 outputs
Outputs of similar age
#99,294
of 276,714 outputs
Outputs of similar age from BMC Family Practice
#1
of 1 outputs
Altmetric has tracked 13,897,020 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,396 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 59% 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 276,714 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them