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A longitudinal design was used, with pre-pandemic predictors measured in 2016 and resilience measured in 2020. Three machine learning algorithms (LASSO, Ridge, and Random Forest) were trained with five-fold cross-validation. SHAP values were used to interpret feature importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LASSO had the best model fit (RMSE\u2009=\u20090.873; R2 = 0.195). Twenty-four features emerged as important predictors. Psychological dispositions and resources, including four Big Five personality traits, optimism, purpose in life, life satisfaction, and religiosity, were strong predictors of resilience. Pre-pandemic social participation, social support, and neighborhood cohesion were also positively associated with resilience. Several indicators of technology adaptation, particularly learning a new device, and socio-behavioral adaptation during the pandemic were additional positive predictors of resilience. In contrast, older subjective age was linked to lower resilience. Several non-linear and interaction effects were identified.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Implications<\/jats:title>\n                    <jats:p>Study findings underscore the complex, multifactorial nature of resilience and demonstrate the value of theory-informed data science approach in advancing our understanding of resilience. Addressing digital inequities and fostering supportive social relationships and community participation are potential targets for population-based strategies as we face increasing threats from disasters.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/geront\/gnaf241","type":"journal-article","created":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:09:56Z","timestamp":1762002596000},"source":"Crossref","is-referenced-by-count":2,"title":["Predicting psychological resilience in older adults during the COVID-19 pandemic: a machine learning approach"],"prefix":"10.1093","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4926-4707","authenticated-orcid":false,"given":"Xiaoling","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Social Work, University of Michigan , Ann Arbor, Michigan,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4886-0918","authenticated-orcid":false,"given":"Xuan","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Information Science, University of Arizona , Tucson, Arizona,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2282-8730","authenticated-orcid":false,"given":"Xiwen","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Literature, Science, and the Arts, University of Michigan , Ann Arbor, Michigan,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"2025121218515546500_gnaf241-B1","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1093\/geronb\/gbaa069","article-title":"A systematic review and meta-analysis of subjective age and the association with cognition, subjective well-being, and depression","volume":"76","author":"Alonso Debreczeni","year":"2021","journal-title":"The Journals of Gerontology. 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