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Opinion versus practice regarding the use of rehabilitation services in home care: an investigation using machine learning algorithms

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2015
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters

Citations

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10 Dimensions

Readers on

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45 Mendeley
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Title
Opinion versus practice regarding the use of rehabilitation services in home care: an investigation using machine learning algorithms
Published in
BMC Medical Informatics and Decision Making, October 2015
DOI 10.1186/s12911-015-0203-1
Pubmed ID
Authors

Lu Cheng, Mu Zhu, Jeffrey W. Poss, John P. Hirdes, Christine Glenny, Paul Stolee

Abstract

Resources for home care rehabilitation are limited, and many home care clients who could benefit do not receive rehabilitation therapy. The interRAI Contact Assessment (CA) is a new screening instrument comprised of a subset of interRAI Home Care (HC) items, designed to be used as a preliminary assessment to identify which potential home care clients should be referred for a full assessment, or for services such as rehabilitation. We investigated which client characteristics are most relevant in predicting rehabilitation use in the full interRAI HC assessment. We applied two algorithms from machine learning and data mining - the LASSO and the random forest - to frequency matched interRAI HC and service utilization data for home care clients in Ontario, Canada. Analyses confirmed the importance of functional decline and mobility variables in targeting rehabilitation services, but suggested that other items in use as potential predictors may be less relevant. Six of the most highly ranked items related to ambulation. Diagnosis of cancer was highly associated with decreased rehabilitation use; however, cognitive status was not. Inconsistencies between variables considered important for classifying clients who need rehabilitation and those identified in this study based on use may indicate a discrepancy in the client characteristics considered relevant in theory versus actual practice.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 %
Japan 1 2%
Unknown 44 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 11%
Student > Master 5 11%
Student > Doctoral Student 4 9%
Student > Bachelor 4 9%
Professor 3 7%
Other 11 24%
Unknown 13 29%
Readers by discipline Count As %
Medicine and Dentistry 7 16%
Nursing and Health Professions 6 13%
Social Sciences 4 9%
Computer Science 2 4%
Mathematics 2 4%
Other 8 18%
Unknown 16 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 24 October 2015.
All research outputs
#12,937,167
of 22,830,751 outputs
Outputs from BMC Medical Informatics and Decision Making
#877
of 1,989 outputs
Outputs of similar age
#123,883
of 278,742 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#18
of 35 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,989 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 53% 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 278,742 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 54% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.