A preliminary test of the therapeutic potential of written exposure with rescripting for generalized anxiety disorder
Bibliographic record
Abstract
This experiment tested a novel written exposure intervention for generalized anxiety disorder (GAD) that consisted of guided rescripting of participants’ worst fear. After describing their worst fear, adults with GAD ( N = 79) were randomly assigned to one of three writing interventions, each consisting of three sessions on consecutive days: (1) standard written exposure (WE), (2) written exposure with rescripting (RWE), and (3) neutral control writing (NC). Measures of symptoms and worry-associated processes were administered at pre- and post-intervention, and at 1-week and 1-month follow-ups. Worry declined significantly in all three conditions. Participants in WE reported significant reductions in fear of anxiety, whereas those in RWE reported significant reductions in fear of anger. Participants in RWE and NC reported a significant decrease in fear of positive emotion. Following RWE, participants perceived their feared scenario as less costly and perceived themselves as better able to cope with it, whereas participants in the WE and NC did not show these changes. Cognitive avoidance, intolerance of uncertainty, and negative problem orientation did not change. Findings suggest overall, RWE was not superior to WE, and that more research is needed to assess their therapeutic potential. Strengths and limitations are discussed for the benefit of future research on exposure for GAD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".