Virtual Reality Exposure Therapy for Reducing School Anxiety in Adolescents: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Virtual reality exposure therapy (VRET) is a promising treatment approach for anxiety disorders. However, while its efficacy has been demonstrated in adults, research on the efficacy of VRET in the treatment of adolescents with anxiety disorders is largely lacking. OBJECTIVE: A pilot study was carried out to test whether exposure to a virtual reality (VR) school environment elicits state anxiety and autonomic arousal in adolescents with school anxiety (diagnoses covering social anxiety disorder or specific phobia involving school contexts). In addition, we examined whether repeated VR exposure led to a reduction in this fear response, trait school anxiety, and social anxiety symptoms. Moreover, the relationship of presence, the subjective sense of "being there," during VR exposure with anxiety measures and treatment response was examined. METHODS: In a pilot study, 10 adolescents with school anxiety (age range 14 to 17 years) participated in five VRET sessions. Self-reported state anxiety, heart rate, and presence during exposure, as well as trait school anxiety and social anxiety before and after treatment, were measured. RESULTS: =0.74) and social anxiety symptoms (d=0.82) as well as a trend toward a decrease in trait school anxiety were observed, while autonomic arousal did not change. In addition, presence during VR exposure was associated with state anxiety and treatment response. CONCLUSIONS: Our findings indicate the feasibility and potential effectiveness of VRET as a treatment method for symptoms of school and social anxiety in adolescents.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".