“Our similarities are different” The relationship between alexithymia and depression
Bibliographic record
Abstract
Alexithymia is a multi-faceted personality trait, which is the inability to recognize and describe emotions. It is associated with a multitude of mental health problems, and its implication for the diagnosis and treatment of depression remains unclear. The current study explored the nuances of the relationship between alexithymia and depression in a sample of 210 patients with depression. We assessed alexithymia with the 20-Item Toronto Alexithymia Scale (TAS-20) and depression with the Beck Depression Inventory (BDI-I). The mean TAS-20 score was 57.47 ± 10.63, and the mean BDI-I score was 49.33±9.24. We explored the network structure of alexithymia and depression. Items related to difficulties in identifying, describing, and expressing feelings were prominent in the alexithymia network. Joy, guilt, and self-dislike stand out in the depression network. In our analysis, we were able to show the crescent relationship between depression and alexithymia, with an inflection point at a TAS-20 score of 53. Although the correlation-concordance index was moderate (0.41; 95 %CI: 0.29-0.51), both scales greatly overlap. In the joint network of alexithymia and depression, we could identify bridge (i.e., connecting) items between alexithymia and depression. These were difficulties understanding and relating feelings to physical and body sensations on the alexithymia side, and self-dislike, crying, and somatic concern on the depression side. Taken together, they point to the pivotal role of alexithymia in the somatization/embodiment of emotions and feelings in depression.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".