Prediction of Social Engagement in Long-Term Care Homes by Sex: A Population-Based Analysis Using Machine Learning.
The objective of this study was to use population-based clinical assessment data to build and evaluate machine-learning models for predicting social engagement among female and male residents of long-term care (LTC) homes. Routine clinical assessments from 203,970 unique residents in 647 LTC homes i...
| Published in: | Journal of Applied Gerontology Vol. 44; no. 6; pp. 902 - 916 |
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| Main Authors: | , , , , |
| Format: | Article |
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Sage Publications Inc.
Jun2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=185002009&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 185002009 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07334648 JGR jtl: Journal of Applied Gerontology issn: 07334648 maglogo: Y pubinfo: dt: Jun2025 vid: 44 iid: 6 pid: 344 pub: Sage Publications Inc. artinfo: ui: 185002009 10.1177/07334648241290589 ppf: 902 ppct: 14 formats: tig: atl: Prediction of Social Engagement in Long-Term Care Homes by Sex: A Population-Based Analysis Using Machine Learning. aug: au: Abedi, Ali Khan, Shehroz S. Iaboni, Andrea Bronskill, Susan E. Bethell, Jennifer affil: KITE Research Institute, Toronto Rehabilitation Institute, 7989 University Health Network, Toronto, ON, Canada ICES, Toronto, ON, Canada Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada su: Ontario Cross-sectional method Health status indicators Sex distribution Nursing care facilities Social participation Psychosocial factors Cognition Old age Nursing home residents Random forest algorithms Secondary analysis Prediction models T-test (Statistics) Research funding Functional status Descriptive statistics Machine learning Physical activity sug: subj: Cross-sectional method Health status indicators Sex distribution Nursing care facilities Social participation Psychosocial factors Cognition Old age Ontario Nursing Care Facilities (Skilled Nursing Facilities) Community care facilities for the elderly Nursing home residents Random forest algorithms Secondary analysis Prediction models T-test (Statistics) Research funding Functional status Descriptive statistics Machine learning Physical activity keyword: female machine learning male nursing homes social participation female machine learning male nursing homes social participation ab: The objective of this study was to use population-based clinical assessment data to build and evaluate machine-learning models for predicting social engagement among female and male residents of long-term care (LTC) homes. Routine clinical assessments from 203,970 unique residents in 647 LTC homes in Ontario, Canada, collected between April 1, 2010, and March 31, 2020, were used to build predictive models for the Index of Social Engagement (ISE) using a data-driven machine-learning approach. General and sex-specific models were built to predict the ISE. The models showed a moderate prediction ability, with random forest emerging as the optimal model. Mean absolute errors were 0.71 and 0.73 in females and males, respectively, using general models and 0.69 and 0.73 using sex-specific models. Variables most highly correlated with the ISE, including activity pursuits, cognition, and physical health and functioning, differed little by sex. Factors associated with social engagement were similar in female and male residents. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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