Cognitive Restructuring Before Versus After Exposure: The Effect on Expectancy and Outcome in Individuals with Claustrophobia
Bibliographic record
Abstract
Several strategies have been proposed for optimizing learning during exposure therapy. One strategy, expectancy violation, is thought to facilitate learning by maximizing the discrepancy between expected and actual outcomes of exposure. In the inhibitory learning model, Craske et al. (2014) suggest that engaging in cognitive restructuring (CR) prior to exposure will prematurely reduce expectancy (i.e., how much one believes a feared outcome will occur) and mitigate outcomes. Instead, they recommend reserving CR for after exposure to consolidate learning. To the best of my knowledge, this assumption has not been tested. The goal of this study was to examine whether CR before exposure prematurely reduces expectancy and mitigates outcomes. Participants (N = 93) endorsing all DSM-5 criteria or all DSM-5 criteria except clinically significant distress or impairment for specific phobia (enclosed places) attended an intervention bookended by pretreatment and posttreatment assessments, and a 1-month follow-up assessment. Participants were randomized to one of two interventions (1) the CR Before condition: 15 minutes of CR before exposure or (2) the CR After condition: 15 minutes of CR after exposure. A 15-minute filler task of self-report questionnaires was used as a no-treatment comparison: the CR Before condition completed the filler task after exposure and the CR After condition completed the filler task before exposure. Participants rated their expectancy (e.g., “I might suffocate,” “I might become trapped”) from 0% to 100% before and after engaging in either CR or the filler task before exposure. Next, participants engaged in six 5- minute tailored exposure trials using a claustrophobic chamber. The Claustrophobia Questionnaire (Radomsky et al., 2001), a Behavioural Approach Test, and the Claustrophobia General Cognitions Questionnaire (Febrarro & Clum, 1995) were used to measure treatment outcomes. It was hypothesized that the CR Before condition would experience greater expectancy reduction than the CR After condition before exposure, and therefore less improvement in claustrophobia outcomes at posttreatment and follow-up than the CR After condition. Results demonstrated that the CR Before condition had greater expectancy reduction than the CR After condition. However, both groups experienced similar and large significant improvement at posttreatment with gains achieved at follow-up. Implications are discussed.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".