Alexithymia and its relative factors of the army patients with mental disorder
Bibliographic record
Abstract
Objective To investigate the factors relating to the alexithymia of the army patients with mental disorder. Methods The 36 patients with schizophrenia, 32 patients with affective disorders and 30 control subjects were assessed with Toronto Alexithymia Scale (TAS), Eysenck Personality Questionnaire (EPQ) and Symptom Checklist 90 (SCL-90). Multiple correlation analysis was used as the major statistical methods. Results TAS total score and the scores of factorⅠ, Ⅱ and Ⅳ were higher in study group (78.27±6.98/79.42±7.21, 3.42±0.56/4.04±0.68, 3.23±0.64/3.36±0.58, 2.87±0.56/2.61±0.59) than those in control group (68.68±7.24, 2.45±0.46, 2.15±0.43, 2.24±0.45, P0.05 or 0.01). The score of factor Ⅲ in the group of patients with affective disorders(4.10±0.71) were higher than that in control group (2.82±0.68, P0.01). There was significant correlation between personality traits and alexithymia. The more the patients with schizophrenia were introversion, the more they were deficient in the ability to describe emotion. The more the patients with affective disorders were introversion, the more they were deficient in the ability to recognize and distinguish between emotion and body feel. In the group of patients with schizophrenia, the correlation coefficients between interpersonal sensitivity and TAS factor I, depression and TAS factir Ⅱ were 0.250, 0.429 respectively. In the group of patients with affective disorders, the correlation coefficients between paranoid ideation and TAS factir Ⅲ, psychoticism and TAS factir Ⅳ were 0.482, 0.498 respectively. Conclusion The patients with schizophrenia and the affective disorders showed alexithymia is related to personality characteristic and psychological health 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 | 0.001 |
| 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) | 0.002 | 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".