Bibliographic record
Abstract
Individuals with severe health anxiety (HA) disproportionately believe that they have, or may acquire, a serious illness. Additionally, individuals with severe HA often engage in somatization, which is the tendency to report physical symptoms that do not have a detectable cause. Past research has established that anxiety sensitivity and somatosensory amplification contribute to HA. Other variables, such as intolerance of uncertainty (IU) and metacognitive beliefs, have been recently associated with HA. However, these factors have not been assessed together in a single study. Through self-report questionnaires, the present study examined whether IU, metacognitions, and cognitive avoidance are associated with HA in a university sample (N = 564). Cognitive avoidance was included as an exploratory variable. Using a hierarchical regression analysis, metacognitions about illness beliefs, and metacognitions about the uncontrollability of thoughts, uniquely predicted HA when controlling for both anxiety sensitivity and somatosensory amplification. An additional hierarchical regression analysis determined that metacognitions about illness, metacognitions about the uncontrollability of thoughts, and metacognitions about biased beliefs uniquely predicted somatization when controlling for anxiety sensitivity and somatosensory amplification. IU, and cognitive avoidance, did not emerge as unique predictors for either HA or somatization. These results indicate that both researchers and clinicians may wish to further explore the role of metacognitive beliefs in the development and maintenance of HA. Department: Psychology Faculty Mentor: Dr. Alexander Penney
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".