[Effect of alexithymia on health anxiety: Mediating role of cognition and meta-cognition].
Bibliographic record
Abstract
OBJECTIVE: According to the cognitive behavior theory and meta-cognitive theory of health anxiety, to examine the association between alexithymia, cognition factors or meta-cognition factors and health anxiety. Methods: A total of 1 164 medical students were investigated by the Short Health Anxiety Inventory, the Health Cognitions Questionnaire, the Meta-cognitions about Health Questionnaire and the Toronto Alexithymia Scale. Results: 1) Correlation analysis showed that alexithymia, dysfunctional beliefs, meta-cognition were significantly positively correlated with health anxiety (r=0.227-0.477, all P<0.01); 2) The results of structural equation model indicated that alexithymia could not exert effects on health anxiety directly (β=-0.05, 95% CI -0.123 to 0.021). The alexithymia could exert effects on health anxiety indirectly not only through dysfunctional beliefs (β=0.192, 95% CI 0.156 to 0.235), but also through the chain-mediated effect of dysfunctional beliefs and meta-cognitions (β=0.103, 95% CI 0.077 to 0.135). Dysfunctional beliefs fully mediated the relation between alexithymia and health anxiety (β=0.247, 95% CI 0.196 to 0.290). Conclusion: Alexithymia can affect health anxiety through the mediating effects of dysfunctional beliefs and meta-cognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".