Evidence map›Paper›PMID 40857478›Full record

ArticleJournal of substance use2025

Predictors of Enacted Stigma Following Disclosure Among People in Recovery From Opioid Use Disorder: A Machine Learning Approach.

Mohammad Mousavi, Ethel Virginia Sticinski, E Carly Hill, Natalie M Brousseau, Jessica Hulsey, Lynn M Morrison, John F Kelly, Annie B Fox, Valerie A Earnshaw

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of substance use, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mohammad MousaviDepartment of Human Development and Family Sciences, University of Delaware, 111 Alison Hall West, Newark, DE 19716, USA.
Ethel Virginia SticinskiDepartment of Human Development and Family Sciences, University of Delaware, 111 Alison Hall West, Newark, DE 19716, USA.
E Carly HillDepartment of Human Development and Family Sciences, University of Delaware, 111 Alison Hall West, Newark, DE 19716, USA.
Natalie M BrousseauInstitute for Collaboration on Health, Intervention, and Policy, University of Connecticut, 2006 Hillside Road, Unit 1248, Storrs, CT 06269-1248, USA.
Jessica HulseyAddiction Policy Forum, Bethesda, MD 20816, USA.
Lynn M MorrisonBrandywine Counseling and Community Services, 2713 Lancaster Avenue, Wilmington, DE 19805, USA.
John F KellyRecovery Research Institute, Massachusetts General Hospital and Harvard Medical School, 151 Merrimac St, 6th Floor, Boston, MA 02114, USA.
Annie B FoxSchool of Healthcare Leadership, MGH Institute of Health Professions, Boston, MA 02129, USA.
Valerie A EarnshawDepartment of Human Development and Family Sciences, University of Delaware, 111 Alison Hall West, Newark, DE 19716, USA.

Funding

Mentoring in Patient-Oriented Addiction ResearchK24AA022136 · NIAAA · MASSACHUSETTS GENERAL HOSPITAL · PI KELLY, JOHN F. · 2014 to 2023
$1.8M
Understanding and Addressing Disclosure to Members of Social Networks among People Recovering from Substance Use DisordersK01DA042881 · NIDA · UNIVERSITY OF DELAWARE · PI EARNSHAW, VALERIE ANN · 2017 to 2020
$756k
NIAAA NIH HHS K24 AA022136NIDA NIH HHS K01 DA042881
6 · The paper itself

Abstract

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.

Indexed as

Enacted StigmaMachine LearningOpioid Use DisorderRecovery

Identifiers

PMID40857478
PMCPMC12311684

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.