{"id":20560,"date":"2024-10-12T17:05:12","date_gmt":"2024-10-12T21:05:12","guid":{"rendered":"https:\/\/www.ices.on.ca\/?post_type=journal_article&#038;p=20560"},"modified":"2024-10-18T17:12:08","modified_gmt":"2024-10-18T21:12:08","slug":"prediction-of-social-engagement-in-long-term-care-homes-by-sex","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/prediction-of-social-engagement-in-long-term-care-homes-by-sex\/","title":{"rendered":"Prediction of social engagement in long-term care homes by sex: a population-based analysis using machine learning"},"content":{"rendered":"<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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, [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[59,32],"migration-helper-qa-sample-set":[],"class_list":["post-20560","journal_article","type-journal_article","status-publish","hentry","topic-data-science","topic-long-term-care-and-continuing-care"],"acf":{"citation":"Abedi A, Khan SS, Iaboni A, Bronskill SE, Bethell J. <em>J Appl Gerontol<\/em>. 2024; 7334648241290589. Epub 2024 Oct 12.","source_url":"https:\/\/doi.org\/10.1177\/07334648241290589","ices_scientist":[1159,1160],"site":[6735],"research_program":[6740],"news_release":"","journal_article":"","atlas":"","research_report":"","infographic":"","video":"","downloads":null,"links":null,"sitecore_item_id":"","sitecore_item_name":"","sitecore_field_values":"","previous_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ICES | Prediction of social engagement in long-term care homes by sex: a population-based analysis using machine learning<\/title>\n<meta name=\"description\" content=\"The objective of this study was to use population-based clinical assessment data to build and evaluate machine-learning models for predicting social\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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