Exploring metacognitions in health anxiety and chronic pain: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: The occurrence of health anxiety (HA) in chronic pain is associated with adverse outcomes. As such, it is important to identify constructs that might influence HA and pain-related outcomes. Metacognitions are an emerging area of interest in both HA and chronic pain, but the relationship between the three factors has not been extensively examined. The current study sought to examine the role of metacognitions about health in HA and pain-related outcomes in chronic pain. METHODS: This study utilized a cross-sectional design. Undergraduate students with self-reported chronic pain (n = 179) completed online measures of HA, pain intensity, pain disability, and metacognitions about health. RESULTS: Regression analyses indicated that both metacognitions about biased thinking and that thoughts are uncontrollable predicted HA in chronic pain, while only metacognitions about biased thinking predicted pain-related disability beyond pain intensity. CONCLUSION: Results demonstrate that HA and pain-related disability are not associated when taking metacognitions about health into account, suggesting that metacognitions about health at least partially account for the relationship between the two. Further, results suggest that metacognitions about biased thinking may independently influence HA and pain-related disability within chronic pain.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".