Can you believe it? Examining the influence of safety behavior beliefs on speech task outcomes
Bibliographic record
Abstract
Beliefs and expectations about treatment have been shown to significantly impact treatment outcomes in medical settings. However, researchers have seldom examined the role of beliefs within the context of cognitive behavioral therapy. Beliefs may be particularly salient for safety behavior (SB) use in exposure therapy, as clinicians often hold opinions about whether judicious SB use facilitates or inhibits treatment. These beliefs may consequently be relayed during psychoeducation, influencing client expectations of SB helpfulness and exposure efficacy. We investigated experimentally the influence of SB beliefs on working memory, speech predictions, speech duration, anxiety, performance, and speech acceptability. Speech anxious undergraduate participants ( N = 144) received psychoeducation on exposure and were told (using random assignment) either that SBs: increase anxiety (unhelpful), decrease anxiety (helpful), or were provided with no information on SBs (control). People in the helpful condition only believed the exposure would be more successful. Crucially, exposure expectancy mediated the relationship between the helpful (but not unhelpful) condition and willingness to engage in future exposures. There were no effects of condition on most cognitive, emotional, or behavioral outcomes, suggesting that SBs (and SB beliefs) may have less impact on exposure outcomes than is currently believed.
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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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".