Relationship between alexithymia and symptoms factor of SCL-90 in patients with depression
Bibliographic record
Abstract
Objective To explore the relationship between alexithymia and symptoms factor of SCL-90 in patients with depression.Methods 40 patients with depression were assessed with Symptom Checklist 90(SCL-90) and Toronto Alexithymia Scale(TAS).Total score and factor scores of the two scales were analyzed by method of correlation analysis and regression analysis.Results Factor II of TAS was positively related with five symptom factors of SCL-90(r=0.333,0.379,0.369,0.418,0.395 respectively;P0.05 or 0.01).Factor III of TAS was negatively related with eight symptom factors of SCL-90(r=-0.509,-0.463,-0.364,-0.395,-0.414,-0.463,-0.456,-0.484;P0.01).Stepwise regression analysis showed that the variance of five symptom factors of SCL-90 explained by factor Ⅱ of TAS varied from 11.0% to 17.5%,the variance of eight symptom factors of SCL-90 explained by factor III of TAS varied from 13.2% to 25.9%.Conclusion Lack of ability to distinguish emotions and body reception can influence the symptom factors in patients with depression.The effect of introversive imagination on psychosomatic symptoms maybe specially obvious.
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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 | 0.003 |
| 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".