Treatment Responsivity in Adolescents With Disruptive Behavior Problems: Co-Creation of a Virtual Reality–Based Add-On Intervention
Bibliographic record
Abstract
BACKGROUND: We developed Street Temptations (ST) as an add-on intervention to increase the treatment responsivity of adolescents with disruptive behavior problems. ST's primary aim is to improve adolescents' mentalizing abilities in order to help them engage in and benefit from psychotherapy. Additionally, virtual reality (VR) is used to work in a more visual, less verbal, fashion. OBJECTIVE: By recapping the lessons learned while developing ST so far, we aim to design the following study on ST. Furthermore, we aim to enhance the development and study of new health care interventions in clinical practice, together with adolescents as their end users. METHODS: We followed an iterative co-creation process to develop a prototype of ST, in collaboration with adolescents and professionals from a secured residential facility in Amsterdam, the Netherlands. The prototype was tested during a pilot phase, involving 2 test runs, in which 4 adolescents and 4 professionals participated. Qualitative data were collected through interviews with the adolescents and by conducting a group interview with the professionals, in order to gain first insights into ST's usability, feasibility, and its added value to clinical practice. In between the first and second test runs, the prototype was enhanced. On the basis of the complete pilot phase, we reflected on the future development and implementation of ST to design a subsequent study. RESULTS: Over the course of 6 months, ST's first prototype was developed during multiple creative sessions. Included was the development of a short 360° VR video, to serve as a base for the mentalization exercises. The final version of ST consisted of 7 individual therapy sessions, incorporating both the VR video and a VR StreetView app. On the basis of the qualitative data collected during the pilot phase, we found preliminary signs of ST's potential to support adolescents' perspective-taking abilities specifically. Additionally, using VR to focus on real-life situations that adolescents encounter in their daily lives possibly helps to facilitate communication. However, several challenges and requests concerning the VR hardware and software and the implementation of ST emerged, pointing toward further development of ST as an add-on intervention. These challenges currently limit large-scale implementation, resulting in specific requirements regarding a subsequent study. CONCLUSIONS: In order to gather more extensive information to shape further development and study treatment effects, a small-scale and individually oriented research design seems currently more suitable than a more standard between-subjects design. Using the reflection on the lessons learned described in this report, a research protocol for a forthcoming study on ST has been developed. By presenting our co-creation journey thus far, we hope to be of inspiration for a more co-creative mindset and in that way contribute to the mutual reinforcement of science and clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".