Using Machine Learning to Identify Educational Predictors of Career and Job Satisfaction in Adults with Disabilities

Beau LeBlond, Bryan R. Christ, Benjamin Ertman, Olivia Chapman, Rea Pillai and Paul B. Perrin ()
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Beau LeBlond: School of Data Science, University of Virginia, Charlottesville, VA 22903, USA
Bryan R. Christ: School of Data Science, University of Virginia, Charlottesville, VA 22903, USA
Benjamin Ertman: Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA
Olivia Chapman: Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA
Rea Pillai: Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA
Paul B. Perrin: School of Data Science, University of Virginia, Charlottesville, VA 22903, USA

Disabilities, 2025, vol. 5, issue 2, 1-15

Abstract: Purpose : This study explored the potential long-term effects of academic-related variables, including academic satisfaction, college degree attainment, unmet academic accommodation needs, and demographic characteristics on the job and career satisfaction of adults with disabilities using modern machine learning techniques. Method : Participants ( n = 409) completed an online survey assessing these constructs. All had a disability or chronic health condition, had attended school in the U.S. throughout their K-12 education, and were between 19 and 86 years of age. Results : The random forest models had 68.6% accuracy in correctly identifying job satisfaction and 72.5% accuracy in correctly identifying career satisfaction. When using mean decrease in impurity (MDI) and permutation importance to identify statistical predictors, academic satisfaction was the most important predictor of job satisfaction in both MDI and permutation importance, while unmet academic accommodations was the fourth highest predictor for MDI behind academic satisfaction, disability level, and age, but ahead of other demographic variables and college degree status, and the second highest predictor of job satisfaction in permutation importance. For career satisfaction, academic satisfaction accounted for the highest MDI, while unmet academic accommodations ranked fourth. For permutation importance, academic satisfaction ranked first, and unmet academic accommodations ranked fifth behind academic satisfaction, age, college degree status, and disability level. Discussion : Meeting the academic accommodation needs of disabled students is linked with lasting vocational success. This study underscores the associations between unmet academic accommodation needs and future job and career satisfaction, illuminated using novel machine learning techniques. To our knowledge, this is the first investigation of the potential long-term associations between unfulfilled accommodation needs and future job and career satisfaction.

Keywords: disability; career satisfaction; job satisfaction; educational accommodations; machine learning (search for similar items in EconPapers)
JEL-codes: I (search for similar items in EconPapers)
Date: 2025
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