Unfinished business in virtual reality: Development and preliminary evaluation of an empty chair intervention grounded in emotion-focused therapy
Bibliographic record
Abstract
The immersive capabilities of virtual reality (VR) make it a promising medium for psychotherapeutic interventions. This randomized controlled trial aimed to develop and evaluate a VR-based adaptation of Emotion-Focused Therapy for Unfinished Business (EFT-UFB). Specifically, we examined its effects on unresolved emotional experiences, self-compassion, self-protection, self-criticism, and perceived stress. Participants ( N = 52) were randomly assigned to either the intervention group ( n = 26) or a waitlist control group (n = 26), with assessments conducted at pre-test, post-test, and follow-up. The primary between-group analyses revealed no statistically significant differences between the intervention and control groups at post-test (unfinished business: t(50) = −2.00, p = .051, Cohen's d = −0.38, 95 % CI [−0.77, 0.01]) or at follow-up (t(50) = 0.60, p = .551, d = 0.08, 95 % CI [−0.31, 0.47]). Within the intervention group, unfinished business demonstrated a large pre–post reduction that was sustained at follow-up, whereas other effects were small or transient. Effect size estimates suggested small advantages for the intervention arm across several outcomes, including unfinished business (d = −1.01 vs. 0.44 in controls), self-criticism (Inadequate Self d = −0.47), and perceived stress (helplessness d = −0.33; self-efficacy d = 0.37). Other domains, including self-compassion and self-protection, showed negligible or inconsistent differences. While the VR-based EFT-UFB did not outperform the waitlist condition, these pilot findings provide preliminary evidence of feasibility and potential benefits for unfinished business. Importantly, the intervention was effectively delivered by a non-psychotherapist (a trained psychologist), underscoring its potential scalability within digital mental health applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".