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Developing a decision tool to identify patients with personality disorders in need of highly specialized care

Overview of attention for article published in BMC Psychiatry, August 2017
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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 (75th percentile)

Mentioned by

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9 tweeters
facebook
1 Facebook page

Citations

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3 Dimensions

Readers on

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45 Mendeley
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Title
Developing a decision tool to identify patients with personality disorders in need of highly specialized care
Published in
BMC Psychiatry, August 2017
DOI 10.1186/s12888-017-1460-6
Pubmed ID
Authors

M. Goorden, E. M. C. Willemsen, C. A. M. Bouwmans-Frijters, J. J. V. Busschbach, M. J. Noomx, C. M. van der Feltz-Cornelis, C. A. Uyl-de Groot, L. Hakkaart-van Roijen

Abstract

Current guidelines recommend referral to highly specialized care for patients with severe personality disorders. However, criteria for allocation to highly specialized care are not clearly defined. The aim of the present study was to develop a decision tool that can support clinicians to identify patients with a personality disorder in need of highly specialized care. Steps taken to develop a decision tool were a literature search, concept mapping, a meeting with experts and a validation study. The concept mapping method resulted in six criteria for the decision tool. The model used in concept mapping provided a good fit (stress value = 0.30) and reasonable reliability (ρ = 0.49). The bridging values were low, indicating homogeneity. The decision tool was subsequently validated by enrolling 368 patients from seven centers. A multilevel model with a Receiver Operating Characteristic Curve (ROC) was applied. In this way, an easily implementable decision tool with relatively high sensitivity (0.74) and specificity (0.69) was developed. A decision tool to identify patients with personality disorders for highly specialized care was developed using advanced methods to combine the input of experts with currently available scientific knowledge. The tool appeared to be able to accurately identify this group of patients. Clinicians can use this decision tool to identify patients who are in need of highly specialized treatment.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 22%
Student > Bachelor 5 11%
Student > Ph. D. Student 4 9%
Student > Postgraduate 3 7%
Professor 3 7%
Other 10 22%
Unknown 10 22%
Readers by discipline Count As %
Psychology 14 31%
Medicine and Dentistry 8 18%
Nursing and Health Professions 5 11%
Social Sciences 2 4%
Economics, Econometrics and Finance 1 2%
Other 4 9%
Unknown 11 24%

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 06 September 2017.
All research outputs
#3,756,446
of 19,542,586 outputs
Outputs from BMC Psychiatry
#1,407
of 4,071 outputs
Outputs of similar age
#69,872
of 285,166 outputs
Outputs of similar age from BMC Psychiatry
#1
of 1 outputs
Altmetric has tracked 19,542,586 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,071 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one has gotten more attention than average, scoring higher than 65% 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 285,166 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 75% 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