Effects of interpretation training on subjective, behavioural, and physiological measures of anxiety during a self-presentation task in an analogue social anxiety sample
Bibliographic record
Abstract
Negative interpretation biases, defined as a tendency to interpret ambiguous social situations negatively, have been theorized to play a role in the maintenance of social anxiety. Research has shown that computer-based interpretation training tasks can modify negative interpretation biases and that this modification is associated with decreases in subjective ratings of anxiety. Negative interpretation biases have also been shown to decrease following cognitive-behavioural therapy. This study investigated the effects of interpretation training and cognitive restructuring on symptomatology, cognitive processes, behaviour, and physiological reactivity in an analogue social anxiety sample. Seventy-two participants with elevated social anxiety scores were randomized to one of 3 conditions: interpretation training (n = 24), cognitive restructuring (n = 24), and control (n = 24). Although none of the conditions showed a decrease in social anxiety symptomatology, participants in the cognitive restructuring condition evidenced a significant decrease in anxiety-related cognitive processes at the 48-hour follow-up. There were no group differences on subjective distress and self-rated performance on the speech task. However, participants in the cognitive restructuring condition were rated as having higher quality speeches by an objective rater compared to participants in the interpretation training condition. Theoretical and clinical 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".