Does Catastrophizing of Bodily Sensations Maintain Health-Related Anxiety? A 14-Day Daily Diary Study with Longitudinal Follow-Up
Bibliographic record
Abstract
BACKGROUND: Health anxiety is common, impairing, and costly. The role of catastrophizing of bodily sensations (i.e. rumination about, overconcern with, and intolerance of bodily sensations) in maintaining health-related anxiety (i.e. anxiety about perceived health problems) is important, but understudied, in the health anxiety literature. AIMS: The present study investigates the role of catastrophizing of bodily sensations as a maintenance factor for health-related anxiety over time. METHOD: Undergraduates (n = 226 women; n = 226 men) completed a baseline assessment, 14-day daily diary study, and 14-day longitudinal follow-up. RESULTS: Path analysis indicated catastrophizing of bodily sensations maintains health-related anxiety from one month to the next in both men and women. CONCLUSIONS: The present study bridges an important gap between theory and evidence. Results support cognitive behavioral theories and extend cross-sectional research asserting catastrophizing of bodily sensations maintains health-related anxiety over time. A cyclical, self-perpetuating pattern was observed in the present study wherein catastrophizing of bodily sensations and health-related anxiety contribute to one another over time. Results also suggest targeting catastrophizing of bodily sensations may reduce health-related anxiety.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".