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Record W4234711128 · doi:10.2196/33555

The Effects of a Virtual Reality–Based Training Program for Adolescents With Disruptive Behavior Problems on Cognitive Distortions and Treatment Motivation: Protocol for a Multiple Baseline Single-Case Experimental Design

2022· article· en· W4234711128 on OpenAlexvenueno aff
Renée E Klein Schaarsberg, Arne Popma, Ramón Lindauer, Levi van Dam

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCognitionMultiple baseline designDysfunctional familyProtocol (science)Applied psychologyDevelopmental psychologyPsychotherapistIntervention (counseling)Medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Serious disruptive behavior among adolescents is a prevalent and often persistent problem. This highlights the importance of adequate and effective treatment to help adolescents with disruptive behavior problems react less hostile and aggressive. In order to create a treatment environment in which behavioral change can be enhanced, treatment motivation plays an essential role. Regarding treatment itself, a focus on challenging self-serving cognitive distortions in order to achieve behavioral change is important. Street Temptations (ST) is a new training program that was developed to address both treatment motivation and cognitive distortions in adolescents with disruptive behavior problems. One of the innovative aspects of ST is the use of virtual reality (VR) techniques to provide adolescents during treatment with visually presented daily social scenarios to activate emotional engagement and dysfunctional cognitions. By using the VR scenarios as an integral starting point of ST's sessions and transferring the power of the VR experience into playful and dynamic exercises to practice social perspective-taking, adolescents are encouraged to reflect on both their own behavior and that of others. This focus on reflection is grounded in ST's main treatment mechanism to influence treatment motivation and cognitive distortions, namely, mentalizing (ie, reflective functioning). OBJECTIVE: The aim of this study is to describe the research protocol to evaluate the effects of ST on treatment motivation and cognitive distortions. We take a closer look at the use of ST and the methodology used, namely, the repeated single-case experimental design (SCED). METHODS: The effects of ST are studied through a multiple baseline SCED, using both quantitative and qualitative data. In total, 18 adolescents from secure residential youth care facilities and secondary special education schools are randomly assigned to 1 of the 3 different baseline conditions. Throughout the baseline phase (1, 2, or 3 weeks), intervention phase (4 weeks), and follow-up phase (1, 2, or 3 weeks), daily measurements on treatment motivation and cognitive distortions are conducted. Secondary study parameters are assessed before baseline, after intervention, and after follow-up. Qualitative data are collected after intervention, as well as at 3 months and 6 months after the intervention. RESULTS: Data collection for this study started in November 2021 and is planned to be completed by August 2023. The results will be published in peer-reviewed journals and presented at national and international conferences. CONCLUSIONS: ST aims to improve the disruptive behavior problems of adolescents. This study will be the first to gain insights into the effectiveness of ST. The strengths of this study include its thorough and individually focused design (SCED), the focus on a residential as well as a secondary special education setting, and the ecological validity. The implications for practice are discussed. TRIAL REGISTRATION: Central Committee on Research Involving Human Subjects NL75545.029.20. Netherlands Trial Register NL9639; https://www.trialregister.nl/trial/9639. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33555.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.382
GPT teacher head0.526
Teacher spread0.144 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNon-randomized trial
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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