The Effect of Mental Health Induction Supports on Teacher Mental Health Outcomes: A Causal Machine Learning Approach

• In Event: AERA Poster Session Saturday 7:45 am
  In Poster Session: Mentoring, Induction, Novice Teacher Support, and Mental Health

Abstract

We investigate the effects of mental health induction supports for first-year teachers on teacher burnout, depressive symptoms, and anxiety. We used data from the Teacher Prep to Career (TP2C) Study (n = 471) to control for prior assessments of mental health outcomes, other induction supports, and other variables that potentially confound these relationships. We used a doubly robust machine learning approach to leverage the benefits of modern causal evaluation tools designed for observational studies. Findings suggest that mental health induction support significantly reduces teacher burnout (emotional exhaustion and depersonalization) but not depressive symptoms and anxiety. We also compared our findings to those based on conventional analytical methods and missing data strategies.

Authors

• Amota Ataneka, University of Cincinnati

• Benjamin Kelcey, University of Cincinnati

• Leigh McLean, University of Delaware

• Peter A. Youngs, University of Virginia

• Jean Baptiste Habarurema, University of Cincinnati