Are Alexithymia, Depression and Hostility Related?
Bibliographic record
Abstract
Background: Depression correlates positively with alexithymia.Less is known about the relationship between hostility and depression and even less about hostility and alexithymia.The aim of this study is to examine the relationship between alexithymia, hostility and depression.Methods: The study was carried out with 308 subjects: 102 patients suffering from somatic illnesses, 98 depressive patients and 108 healthy people.The mean age of the group was 40.26 years (sd=11.63);184 of them were women and 124 were men.The participants were assessed with the Beck Depression Index (BDI-21), the 20-item Toronto Alexithymia Scale (TAS-20), the Hostility and Direction of Hostility Scale (HDHQ).Data regarding sociodemographic characteristics were also collected.Linear regression was performed to evaluate the relationship between the scores of the participants in each scale.Results: Alexithymia scores correlate positively with depression scores (α= 41.73 and Β= 0.88, p= .000).Hostility scores correlate positively with depression scores (α= 15.66 and Β= 0.39, p= .000).Finally, alexithymia scores correlate positively with hostility scores (α= 31.05 and Β= 1.05, p= .000).Conclusions: The more depressive somebody is, the more alexithymic and hostile he is likely to be; finally, the more hostile someone is, the more alexithymic he is likely to be.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".