The Effect of an Emotion Recognition and Expression Program on the Alexithymia, Emotion Expression Skills and Positive and Negative Symptoms of Patients with Schizophrenia in a Community Mental Health Center
Bibliographic record
Abstract
This study aimed to examine the effect of an emotion recognition and expression program (EREP) on the alexithymia, emotion expression skills and positive and negative symptoms of patients with schizophrenia. The study had a non-randomized, quasi-experimental design including a pretest, post-test, and follow-up test. It was conducted with 36 patients with schizophrenia (n = 18 intervention group, n = 18 control group) who regularly visited a Community Mental Health Center (CMHC) in Türkiye and participated voluntarily. The EREP was applied to the intervention group for eight weeks. "Personal Information Form", "Emotion Expression Scale (EES)", "Toronto Alexithymia Scale (TAS)", and "Positive Negative Syndrome Scale (PANSS)" were applied to all participants in the pretest, post-test, and follow-up test. The follow-up test was applied one month after the end of the sessions. Number, percentage, chi-square test, and repeated measures analysis of variance were used for data evaluation. In the total alexithymia score, there was a significant difference in the group interaction by time in the intervention group compared to the control group. In terms of total alexithymia score, the post-test and follow-up test mean scores of the intervention group were lower than the control group (p < 0.05; η2 = 0.122). There was a significant time*group interaction in the positive emotion subscale of the EES (p < 0.05; η2 = 0.121). The findings of our study indicated that the EREP had a positive effect on the alexithymia scores of patients with schizophrenia. We found that the EREP used in our study contributed to the reduction of alexithymia levels in patients with 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 | 0.000 | 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".