Enhancing employment outcomes for autistic youth: Using machine learning to identify strategies for success

Abstract

BACKGROUND: The employment rates of autistic young adults continue to be significantly lower than that of their neurotypical peers. OBJECTIVE: Researchers in this study sought to identify the barriers and facilitators associated with these individuals’ transition into the workforce to better understand how educators and stakeholders can support students’ post-secondary career plans. METHODS: Investigators used a classification tree analysis with a sample of 236 caregivers of autistic individuals, who completed an online survey. RESULTS: The analysis identified critical factors in predicting successful employment for respondents 21 years and under and those over 21 years old. These factors included: difficulties in the job search process, challenges with relationships at work, resources used, job maintenance, motivation to work, and the application process. CONCLUSION: These findings represent the first use of machine learning to identify pivotal points on the path to employment for autistic individuals. This information will better prepare school-based professionals and other stakeholders to support their students in attaining and maintaining employment, a critical aspect of achieving fulfillment and independence. Future research should consider the perspectives of other stakeholders, autistic individuals and employers, and apply the findings to the development of interventions.