Predictors of enacted stigma following disclosure among people in recovery from opioid use disorder: a machine learning approach.
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, enhanc...
| Publicado en: | Journal of Substance Use Vol. 31; no. 3; pp. 381 - 389 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193710444&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193710444 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14659891 ILV jtl: Journal of Substance Use issn: 14659891 maglogo: Y pubinfo: dt: Jun2026 vid: 31 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 193710444 186435616 193710444 193710444 10.1080/14659891.2025.2529806 193710444 ppf: 381 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predictors of enacted stigma following disclosure among people in recovery from opioid use disorder: a machine learning approach. aug: au: Mousavi, Mohammad Sticinski, Ethel Virginia Hill, E. Carly Brousseau, Natalie M. Hulsey, Jessica Morrison, Lynn M. Kelly, John F. Fox, Annie B. Earnshaw, Valerie A. affil: Department of Human Development and Family Sciences, University of Delaware, Newark, Delaware, USA sug: subj: Substance Use Disorders Rehabilitation Narcotics Self Disclosure Persons with Substance Use Disorders Psychosocial Factors Stigma Evaluation Risk Assessment Prediction Models Machine Learning Recovery Human Funding Source Male Female Adult Middle Age Aged Prospective Studies Convenience Sample Questionnaires Descriptive Statistics Data Analysis Software Support Vector Machine Random Forest Decision Trees Patient Compliance Mental Health Decision Making Quality of Life Regression Scales Time Factors Age Factors Conceptual Framework Stigma Prevention and Control Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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