Bibliographic record
Abstract
Objectives：The purpose of this study was to investigate the deficits of emotional awareness in patients with schizophrenia. In addition, we aimed to examine the relationship between alexithymia, emotional recognition and the other clinical variables in patients with schizophrenia. Methods：Forty patients with schizophrenia and forty normal controls were enrolled in a university-affiliated hospital. The Korean Version of Levels of Emotional Awareness Scale(LEAS-K), Korean Version of 20-item Toronto Alexithymia Scale(TAS-20K), Korean Facial Expressions of Emotion(KOFEE), and Positive and Negative Symptom Scale(PANSS) were performed. To compare the variables of the groups with schizophrenia and normal control, independent ttest for continuous variables and χ 2 test for discrete variables were applied. Results：The patients with schizophrenia had lower scores on LEAS-K and KOFEE and higher score on the TAS-20K than normal controls. Furthermore, there was a significant correlation between LEAS-K(Self) and TAS-20K factor 2(Difficulty Describing Feelings, DDF) among schizophrenic patients. LEAS-K(Total) score was significantly correlated with KOFEE Happy face score. TAS-20K factor 3(Externally Oriented Thinking, EOT) was significantly correlated with KOFEE Happy, Fear, Neutral, Total scores among schizophrenic patients. TAS-20K Factor 1(Difficulty Identifying Feelings, DDF) was also significantly correlated with PANSS-general score. There was a significant correlation between KOFEE Happy face score and PANSS-negative score among schizophrenic patients. Conclusion：Schizophrenic patients are aware of their emotions through bodily sensations or sensorimotor enactive. Our findings support that schizophrenic patients’ emotional awareness level is behind the restricted expression of single emotion. KOFEE(Total) and LEAS-K(Self) will be useful tools to evaluate schizophrenia.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".