Anxiety sensitivity, fear, and avoidance behavior in headache pain
Bibliographic record
Abstract
Recent research has implicated anxiety sensitivity (AS), the fear of anxiety-related sensations, as a mitigating factor involved in fear and avoidance in patients with chronic back pain [Understanding and treating fear of pain (2004) 3]. Given reported similarities between individuals experiencing chronic pain and those experiencing recurrent headaches, it is theoretically plausible that AS plays a role in influencing fear of pain and avoidance behavior in people with recurrent headache. This has not been studied to date. In the current study we used structural equation modeling to examine the role of AS in fear and avoidance behavior of patients experiencing recurrent headaches. Treatment seeking patients with recurrent headaches completed measures of AS, headache pain severity, pain-related fear, and pain-related escape and avoidance behavior. Structural equation modeling supported the prediction of a direct significant loading of AS on fear of pain. Headache severity also had a direct loading on fear of pain. Results also revealed that AS and headache severity had indirect relationships to pain-related escape and avoidance via their direct loadings on fear of pain. Headache severity also had a small direct loading on escape and avoidance behavior. These results provide compelling evidence that AS may play an important role in pain-related fear and escape and avoidance behavior in patients with recurrent headaches.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".