Moderating Role of State and Trait Positive Affect in Virtual Reality Exposure for Public Speaking Anxiety: Protocol for a Multisite Experimental Study
Bibliographic record
Abstract
Abstract Background The identification of moderators of exposure-based cognitive behavioral therapy (EXCBT) outcomes has the potential to (1) guide the judicious application of the limited resource that is EXCBT and (2) identify additional treatment targets for ameliorating the deleterious effects of an identified moderator, thereby enhancing clinical response. Experimental and clinical studies have yielded intriguing findings for the moderating influence of positive affect on EXCBT outcomes. Mixed findings for state positive affect (at the time of extinction or exposure trials) as a predictor of EXCBT outcomes stand in contrast to evidence that baseline levels of trait positive affect may be a more effective predictor. As such, questions remain about the best way to assess positive affect as a potential treatment moderator. Objective This study was designed to investigate (1) the relative value of state and trait positive affect for predicting the outcome of a single-session virtual reality exposure intervention for public speaking anxiety in adults attending college and (2) the role of 3 related constructs—optimism, hopefulness, and mental health self-efficacy—that may explain the predictive significance of trait positive affect. Methods State affect will be manipulated at an experimental level using affect induction procedures; trait positive affect will be measured at baseline. Three dependent measures—a primary outcome of public speaking anxiety and secondary outcomes of social phobia and self-reported valence—will be examined. This study relies on a team science approach and is being conducted across 12 collaborating sites through the Exposure Therapy Consortium, allowing for replication of all findings across diverse study sites worldwide. Results Data collection for this study began in October 2024 and ended in August 2025. We expect to complete data analysis and submit results for publication in approximately November 2025. Conclusions This study will help clarify the relationship among positive affect (state and trait), exposure learning, and 3 related constructs (optimism, hopefulness, and mental health self-efficacy). These findings will illuminate strategies for future treatment improvement and enhanced efficacy.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".