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Application of latent class analysis in assessing the competency of physicians in China

Overview of attention for article published in BMC Medical Education, November 2017
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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7 X users

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10 Dimensions

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27 Mendeley
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Title
Application of latent class analysis in assessing the competency of physicians in China
Published in
BMC Medical Education, November 2017
DOI 10.1186/s12909-017-1039-4
Pubmed ID
Authors

Zhuang Liu, Yue Zhang, Lei Tian, Baozhi Sun, Qing Chang, Yuhong Zhao

Abstract

The physicians' competency is an important public health issue around the world. Several international organizations have taken the lead in examining the competencies required to be a physician. The purpose of this study is to identify subgroups of physicians' competency based upon the importance results of competency evaluation and provide a scientific basis for the qualitative research of the competency of physicians. A cross-sectional study was conducted on a large population-based sample in 31 provinces, autonomous regions and municipalities directly under the central government in China. The latent class analysis was performed to identify patterns of physicians' competency using M-plus software. In this study, the latent class analysis was adopted to identify the appropriate number of distinct latent classes of physicians' competency based on eight competency dimensions, and a four-class model best fit the data, which are excellent competency group, lack of professionalism competency group, individual competency driven group, and lack of competency cognitive group. Therefore, 6247 physicians can be divided into four latent classes based on the importance results of competency evaluation, and the number of each class is 5684, 284, 215 and 64, respectively. These findings suggested that latent class analysis can be used to study the competency of physicians, and four distinct subgroups were identified. Therefore, we can effectively understand the patterns of physicians' competency, and the health administrative departments could utilize more specific measures according to their different competency subgroups, and providing individualized training schemes in the future training and management of physicians.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 19%
Researcher 5 19%
Student > Master 4 15%
Lecturer > Senior Lecturer 2 7%
Professor 1 4%
Other 3 11%
Unknown 7 26%
Readers by discipline Count As %
Medicine and Dentistry 6 22%
Nursing and Health Professions 3 11%
Business, Management and Accounting 2 7%
Mathematics 1 4%
Agricultural and Biological Sciences 1 4%
Other 6 22%
Unknown 8 30%
Attention Score in Context

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 09 December 2017.
All research outputs
#7,119,928
of 23,978,283 outputs
Outputs from BMC Medical Education
#1,215
of 3,607 outputs
Outputs of similar age
#110,983
of 329,217 outputs
Outputs of similar age from BMC Medical Education
#40
of 89 outputs
Altmetric has tracked 23,978,283 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 3,607 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has gotten more attention than average, scoring higher than 66% 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 329,217 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 66% of its contemporaries.
We're also able to compare this research output to 89 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 55% of its contemporaries.