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Related Concept Videos

Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Cognitive Therapy01:25

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Cognitive therapy, pioneered by Aaron T. Beck in the 1960s, is a structured approach to addressing psychological distress by focusing on the influence of thoughts on emotions and behaviors. All cognitive therapies involve the basic assumption that human beings have control over their feelings, and that how individuals feel about something depends on how they think about it. Unlike psychoanalytic methods that delve into unconscious processes or humanistic approaches emphasizing...
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Treatment Strategies for Psychological Disorders01:24

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Treatment approaches for psychological disorders fall into three main categories: psychological, biological, and sociocultural. Each approach targets different aspects of mental health, requiring varying levels of education and training.
Psychological therapies focus on modifying emotions, thoughts, and behaviors through talking, interpreting, listening, rewarding, challenging, and modeling. Clinical psychologists, counselors, and social workers commonly practice psychotherapy. Clinical...
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Updated: Jan 17, 2026

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A multilevel machine learning algorithm to predict session-by-session outcome for patients receiving

Juan Martín Gómez Penedo1, Alice E Coyne2, Manuel Meglio3

  • 1Clinical Psychology and Psychotherapy Department, Universität Osnabrück, Lise-Meitner-Str. 3, 49076, Osnabrück, Germany; Clinical Psychology and Psychotherapy Department, Universität Kassel, Kassel, Germany.

Behaviour Research and Therapy
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict psychotherapy outcomes more accurately. A new tree-based approach integrating multilevel and machine learning models shows promise for personalized mental health care.

Keywords:
Cognitive-behavioural therapyLongitudinalMachine learningMultilevel modelsOutcomePredictive modelsPsychotherapy

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Area of Science:

  • Psychiatry and Mental Health
  • Data Science and Machine Learning
  • Clinical Psychology

Background:

  • Measurement-based care (MBC) systems can be improved with advanced predictive models.
  • Machine learning (ML) offers potential for more accurate, session-by-session psychotherapy outcome predictions.
  • Integrating ML with existing statistical models can enhance MBC effectiveness.

Purpose of the Study:

  • Develop and evaluate a novel tree-based model combining multilevel and ML approaches.
  • Predict patient clinical improvement trajectories in cognitive-behavioural therapy (CBT).
  • Enhance the precision of mental health care through improved treatment planning.

Main Methods:

  • Utilized a dataset of 1008 outpatients from a German CBT university clinic.
  • Employed a generalized linear mixed model tree algorithm for prediction.
  • Randomly split the sample into training (2/3) and testing (1/3) sets for model development and validation.

Main Results:

  • The best model identified 10 patient groups based on baseline characteristics and improvement.
  • The algorithm achieved a correlation of 0.65 between observed and predicted outcomes in the test set (cross-validation R² = 0.42).
  • Failure boundaries correctly identified 67.6% of patients with unreliable improvement within 15 sessions.

Conclusions:

  • Preliminary evidence supports the integration of multilevel and ML models using generalized linear mixed model trees.
  • Developed algorithms can aid in the routine implementation of precision mental health care.
  • These tools can inform therapists' treatment planning and responsiveness to patient needs.