| Sumario: | Objective: Individuals who are in recovery from opioid use disorder experience enacted stigma, which can undermine treatment retention and recovery. Stronger understanding of who is at risk of experiencing enacted stigma can inform intervention efforts to reduce experiences of enacted stigma, enhance wellbeing, and promote treatment outcomes among people in recovery from OUD. The current study applies a machine learning framework to examine predictors of enacted stigma among people in recovery from OUD. Methods: This study employed a longitudinal approach, with n = 112 participants responding to surveys before a possible disclosure and again after three months. We tested three different machine learning models and used a variety of performance metrics to evaluate model performance. Results: The random forest model performed the best with an R-squared of 0.85, indicating that our predictors explained 85% of the variance in enacted stigma. Important predictors of enacted stigma were recovery duration, age, disclosure, current issues with drugs, and sobriety commitment. Conclusions: Individuals who are in recovery for a shorter time, did not disclose, have greater issues with drugs, and are younger were at higher risk of experiencing enacted stigma. Interventions may be needed to address stigma among people with these characteristics in treatment for OUD.
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