The relationship between alexithymia and cognitive coping strategies in depressive patients
Bibliographic record
Abstract
Objective: To investigate the alexithymia,cognitive coping strategy and their relationship in depressive patients. Method: 143 depressive patients were assessed with the Chinese version of cognitive emotion regulation questionnaire (CERQ-C) and the twenty-item version of Toronto alexithymia scale (TAS-20) ,and the results were compared with 95 healthy people (controls) . Results: Compared with controls,depressive patients showed significantly higher scores in the total score and factor scores of TAS-20 (P 0. 01) , higher scores in selfblame,acceptance,rumination,catastrophizing,blaming and lower score in perspective of CERQ-C(P 0. 05) . The factor communication and affection entered the regression equation for factor one of TAS-20. Their parents' education degree and economic status entered the regression equation for factor two of TAS-20. Education and acceptance of CERQ-C entered the regression equation for factor three of TAS-20. The factor communication,affection with relatives entered the regression equation for total score of TAS-20. Conclusion: Depressive patients suffered from significant alexithymia and bad cognitive coping strategies. Cognitive coping strategies,gender and education years influence alexithymia in depressive patients.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".