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Identifying Youth Problem Profiles and Predicting Remission Following Mental Health Treatment.

Holly R Turner1, David S Jackson2, Max Sender2

  • 1Department of Psychology, University of Hawai‛i at Mānoa, 2530 Dole Street, Sakamaki C400, Honolulu, HI, 96822, USA. hollyt87@hawaii.edu.

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Latent profile analysis identified four youth mental health risk groups in public services. Higher-risk profiles predicted lower remission rates for problems and functioning, informing tailored treatment plans.

Keywords:
Latent profile analysisPredicting treatment outcomesPublic mental health careYouth mental health

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Area of Science:

  • Child and Adolescent Psychiatry
  • Mental Health Services Research
  • Quantitative Psychology

Background:

  • Public mental health services serve diverse youth populations with varying needs.
  • Classifying youth based on initial problem profiles can aid in predicting treatment outcomes.
  • Understanding these profiles is crucial for optimizing intervention strategies.

Purpose of the Study:

  • To categorize youth in public mental health settings into distinct, homogenous classes using latent profile analysis.
  • To examine the association between these identified classes and subsequent clinical remission.
  • To determine if initial problem profiles can predict treatment outcomes, including problem severity and functional impairment.

Main Methods:

  • Latent profile analysis (LPA) was applied to caregiver-reported Ohio Scales data for 1090 youth.
  • Youth were classified into distinct profiles based on their mental health problem severity at intake.
  • The predictive validity of these profiles was assessed against treatment remission criteria, including Ohio Scales and Child and Adolescent Functional Assessment Scale (CAFAS) scores.

Main Results:

  • A four-class model best represented the data: low-risk (63.3%), internalizing (23.2%), delinquency (8.8%), and high-risk (4.7%).
  • Youth in the internalizing and high-risk classes showed lower likelihoods of problem remission compared to low-risk and delinquency classes.
  • Youth in the delinquency and high-risk classes had poorer functional impairment remission outcomes than those in the internalizing and low-risk classes.

Conclusions:

  • Initial problem profiles, identified through latent profile analysis, can predict clinical remission in public mental health care.
  • Class membership serves as a valuable tool for clinicians to anticipate treatment trajectories.
  • These class-based predictions can inform the development of individualized treatment plans and facilitate timely adjustments to care.