five

Data_Sheet_1_Network Analysis of the Brief ICF Core Set for Schizophrenia.pdf

收藏
NIAID Data Ecosystem2026-03-13 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Network_Analysis_of_the_Brief_ICF_Core_Set_for_Schizophrenia_pdf/20086577
下载链接
链接失效反馈
官方服务:
资源简介:
BackgroundThe International Classification of Functioning, Disability, and Health Core Sets (ICF-CSs) for schizophrenia are a set of categories for assessing functioning in persons with this health condition. This study aimed to: a) estimate the network structure of the Brief ICF-CS for schizophrenia, b) examine the community structure (categories strongly clustered together) underlying this network, and c) identify the most central categories within this network. MethodsA total of 638 health professionals from different backgrounds and with a significant role in the treatment of individuals with schizophrenia participated in a series of Delphi studies. Based on their responses we used the Ising model to estimate the network structure of the 25-category Brief ICF-CS, and then estimated the degree of centrality for all categories. Finally, the community structure was detected using the walktrap algorithm. ResultsThe resulting network revealed strong associations between individual categories within components of the ICF (i.e., Body functions, Activities and participation, and Environmental factors). The results also showed three distinct clusters of categories corresponding to the same three components. The categories e410 Individual attitudes of immediate family members, e450 Individual attitudes of health professionals, d910 Community life, and d175 Solving problems were among the most central categories in the Brief ICF-CS network. ConclusionThese results demonstrate the utility of a network approach for estimating the structure of the ICF-CSs. Implications of these results for clinical interventions and development of new instruments are discussed.
创建时间:
2022-06-17
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作