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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
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Attitudinal Predictors of No-Show to Substance Use Disorder Treatment Intake in Veterans.

James M Bjork1,2, Indranil Sahoo3, Angela J Zaur4

  • 1Mental Health Service, Richmond Veterans Affairs Medical Center, Richmond, VA, USA.

Substance Use : Research and Treatment
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

Veterans’ attitudes about substance use disorder (SUD) and treatment can help predict if they will attend their initial intake appointment. Identifying these factors may improve treatment engagement for Veterans with SUD.

Keywords:
addictionattendancemachine learningno-showrisktreatment

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Area of Science:

  • Psychiatry
  • Health Services Research
  • Machine Learning

Background:

  • Substance use disorder (SUD) significantly impacts Veterans, despite available treatment.
  • Many Veterans with SUD miss their initial intake appointments, hindering care access.
  • Predicting no-shows using patient attitudes is an under-researched area.

Purpose of the Study:

  • To assess the feasibility of probing Veteran attitudes about SUD and treatment.
  • To determine if these attitudes can predict missed intake appointments.

Main Methods:

  • A preliminary study surveyed 79 Veterans scheduled for SUD treatment intake.
  • Data collected via mail, app, or interview included attitudes towards SUD and treatment.
  • Machine learning (ML) analyzed responses to predict no-shows.

Main Results:

  • Older age, longer wait times, and low medical adherence predicted no-shows.
  • Attitudinal predictors included high craving, belief in addiction, distrust of past providers, and recovery community involvement.
  • ML identified key attitudinal and demographic factors for predicting no-shows.

Conclusions:

  • Querying Veterans' attitudes is feasible and may predict missed appointments.
  • Further research with larger samples could refine no-show risk assessments.
  • Findings may inform interventions to improve treatment engagement for Veterans with SUD.