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Boundaries Of The Construct Of Unemployment In The Pre-Retirement Years: Exploring An Expanded Measurement Of Lost-Work Opportunity, Maren Wright Voss, Soham Al Snih, Wei Li, Man Hung, Lorie Gage Richards
Boundaries Of The Construct Of Unemployment In The Pre-Retirement Years: Exploring An Expanded Measurement Of Lost-Work Opportunity, Maren Wright Voss, Soham Al Snih, Wei Li, Man Hung, Lorie Gage Richards
Extension Research
There is uncertainty related to whether retirement negatively affects health—possibly due to complexity around retirement decisions. Lost-work opportunity through unemployment or forced retirement has been shown to negatively affect health. Lost-work opportunity can be captured in two measurement fields, either a reported experience of being forced into retirement or reported unemployment. However, 17% of individuals retiring due to the loss of work opportunity identified in qualitative interviewing (i.e., unemployment, temporary lay-offs, company buy-outs, forced relocations, etc.) do not report this unemployment or involuntary retirement in quantitative survey responses. We propose broadening the conceptualization of late-career unemployment to incorporate other lost …
Development Of A Recommender System For Dental Care Using Machine Learning, Man Hung, Julie Xu, Evelyn Lauren, Maren Wright Voss, Megan N. Rosales, Weicong Su, Bianca Ruiz-Negrón, Yao He, Wei Li, Frank W. Licari
Development Of A Recommender System For Dental Care Using Machine Learning, Man Hung, Julie Xu, Evelyn Lauren, Maren Wright Voss, Megan N. Rosales, Weicong Su, Bianca Ruiz-Negrón, Yao He, Wei Li, Frank W. Licari
Extension Research
Resource mismanagement along with the underutilization of dental care has led to serious health and economic consequences. Artificial intelligence was applied to a national health database to develop recommendations for dental care. The data were obtained from the 2013–2014 National Health and Nutrition Examination Survey to perform machine learning. Feature selection was done using LASSO in R to determine the best regression model. Prediction models were developed using several supervised machine learning algorithms, including logistic regression, support vector machine, random forest, and classification and regression tree. Feature selection by LASSO along with the inclusion of additional clinically relevant variables identified …