The Effects of Alexithymia on Self-Reflection and Insight in Major Depressive Disorder
Bibliographic record
Abstract
Abs tractAim: We hypothesized that alexithymic depressive patients have low insight, which correlates with more severe depression and anxiety.In this context, we aimed to explore the correlation between insight and self-reflective abilities, alexithymia, as well as the presence and severity of major depression, which is the most diagnosed psychiatric disease in the world. Methods:We accepted 80 patients diagnosed with major depression who were in outpatient care at our psychiatry clinic between September and December 2020, along with 80 healthy controls.We applied the Toronto Alexithymia Scale (TAS-20), Hamilton Anxiety Rating Scale, Hamilton Depression Rating Scale, and Self-Reflection and Insight Scale (SRIS) to all participants.This study was designed as a cross-sectional observational study.Results: SRIS-insight score was found to be lower (p<0.001) in the patient group; higher scores were observed for difficulty in identifying feelings, difficulty in describing feelings, and the TAS-20 total score (p<0.001).TAS-20-total and subscales were found to be predicted by SRIS-insight in both groups (p<0.001;p<0.01). Conclusion:When clinicians evaluate alexithymic patients with major depression, they need to consider this alongside symptom evaluation, as these patients may have low insight.
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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 | 0.001 |
| 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".