Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling in Therapy01:26

Modeling in Therapy

130
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...
130
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

91
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
91
Behavior Modification01:21

Behavior Modification

210
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
210
Behaviorism01:28

Behaviorism

2.3K
The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
2.3K
Behavior Therapy01:22

Behavior Therapy

86
Behavior therapy incorporates diverse techniques rooted in classical conditioning principles to address maladaptive behaviors and anxiety disorders. These methods aim to reduce avoidance behaviors, foster adaptive coping mechanisms, and alter associations between stimuli and responses, making them effective in a wide range of therapeutic contexts.
Exposure therapy is a cornerstone of behavioral treatment for anxiety disorders. It involves systematic exposure to feared stimuli, either in real...
86
Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

1.6K
The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data...
1.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mental health and psychosocial support during ongoing armed conflict: position paper of the Global Collaboration on Traumatic Stress.

European journal of psychotraumatology·2026
Same author

Population-based RCT of a digital cognitive-behavioural guided self-help intervention for anxiety, depression and eating disorders in college students.

Nature human behaviour·2026
Same author

Preprocessing Large-Scale Conversational Datasets: A Framework and Its Application to Behavioral Health Transcripts.

JMIR formative research·2025
Same author

Detecting Eating Disorders From Social Media Content: What Has Been Done and Where Do We Go Next?

The International journal of eating disorders·2025
Same author

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students.

BMC psychiatry·2024
Same author

Effects of Chatbot Components to Facilitate Mental Health Services Use in Individuals With Eating Disorders Following Online Screening: An Optimization Randomized Controlled Trial.

The International journal of eating disorders·2024

Related Experiment Video

Updated: Jul 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Machine Learning Model to Predict Assignment of Therapy Homework in Behavioral Treatments: Algorithm Development and

Gal Peretz1, C Barr Taylor2,3, Josef I Ruzek2,3

  • 1Eleos Health, Waltham, MA, United States.

JMIR Formative Research
|May 15, 2023
PubMed
Summary

A machine learning (ML) model accurately identified therapeutic homework in behavioral health sessions, improving objective tracking. This AI approach supports evidence-based care and can enhance treatment outcomes by analyzing homework completion in real-world settings.

Keywords:
artificial intelligencebehavioral treatmentdeep learningempirically-based practicehomeworkinterventionmHealthmachine learningmental healthnatural language processingtherapytreatment fidelity

More Related Videos

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Jul 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Behavioral Health
  • Artificial Intelligence
  • Machine Learning

Background:

  • Therapeutic homework is crucial for cognitive and behavioral interventions, yet its use in routine clinical care is understudied.
  • Current research relies on self-reports, lacking objective data on homework in real-world therapy settings.

Purpose of the Study:

  • To test a machine learning (ML) model using natural language processing (NLP) for identifying therapeutic homework in behavioral health sessions.
  • To develop a more objective and accurate method for detecting homework assignments in therapy.

Main Methods:

  • Analyzed 34,497 audio-recorded therapy sessions using an AI platform.
  • Trained an unsupervised sentence embedding model on 2.83 million therapist-client microdialogues from 4000 sessions.
  • Experts validated homework assignments in 100 sessions, achieving 90% agreement.

Main Results:

  • Homework was assigned in 61% of sessions, with 34% having multiple assignments.
  • The ML classifier achieved a 72% F1-score, outperforming existing models.
  • Common homework types included practicing skills, taking action, journaling, and learning new skills.

Conclusions:

  • ML and NLP can significantly improve the detection of therapeutic homework in behavioral health.
  • AI can support therapists in real-world settings, enhancing evidence-based care and intervention fidelity.
  • This approach can be extended to investigate homework's impact on therapeutic outcomes.