A Study of the Characteristics of Alexithymia and Emotion Regulation in Patients with Depression.
Bibliographic record
Abstract
BACKGROUND: Even though patients with depression often show significant alexithymia, the underlying mechanism of their alexithymia remains unclear. Furthermore, few experimental studies have explored their ability to regulate emotions. OBJECTIVE: To explore the characteristics of alexithymia in patients with depression, and the relationship of depressive symptoms, alexithymia and emotion regulation. METHODS: A total of 36 patients with depression and 31 healthy controls were enrolled. HAMD-24 and HAMA were used to evaluate depressive and anxious symptoms. Toronto Alexithymia Scale (TAS) was employed to assess alexithymia. A computer experiment was used to evaluate emotion regulation. RESULTS: =0.043); while under watch-negative, negative-reappraisal and negative-suppression conditions, the ratings of patients with depression showed no difference from those of the controls. The scores of TAS were correlated with the HAMD-24 scores and the HAMA scores significantly in patients with depression. However, the ratings on the emotional regulation experiment had no correlation with the HAMD-24 scores, the HAMA scores or the TAS scores. CONCLUSION: The incidence of alexithymia is higher in patients with depression than the general population. The depressive symptoms may have interplay with alexithymia in patients with depression. Emotion regulation ability may be an independent trait and have nothing to do with the depressive state.
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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 | 0.000 | 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".