The Nature of the Association between Anxiety Sensitivity and Pain-Related Anxiety: Evidence from Correlational and Intervention Studies
Bibliographic record
Abstract
High anxiety sensitivity (AS) has been associated with elevated pain-related anxiety in anxiety and pain samples. The present study investigated (a) the associations among the lower order dimensions of AS and pain-related anxiety, using a robust measure of AS, and (b) the pain-related anxiety outcomes of a telephone-delivered cognitive behavioural treatment (CBT) designed to reduce high AS. Participants were 80 anxiety treatment-seeking participants with high AS (M age = 36 years; 79% women). After providing baseline data on AS and pain-related anxiety, participants were randomly assigned to an eight-week telephone CBT or a waiting list control. At baseline, bivariate correlations showed AS physical and cognitive, but not social, concerns were significantly associated with pain-related fear and arousal but not escape/avoidance behaviours. Multiple regression revealed that after accounting for emotional distress symptoms, AS physical, but not cognitive or social, concerns uniquely predicted pain-related anxiety. Multilevel modelling showed that the AS-targeted CBT reduced pain-related anxiety and treatment-related changes in global AS and AS physical concerns mediated changes in pain-related anxiety. Results suggest that an AS-targeted intervention may have implications for reducing pain-related anxiety. Further research is needed in a chronic pain sample.
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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 | 0.025 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".