Emotion Recognition, Emotion Awareness, Metacognition, and Social Functioning in Persons with Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Emotion processing has received significant research attention in persons with schizophrenia. However, some aspects of this construct, such as emotion awareness, are less researched. In addition, there is limited work on metacognitive awareness and social functioning in persons with schizophrenia. METHODS: Our sample comprised of 27 participants with schizophrenia- and 26 nonclinical controls. The clinical group was assessed on Scale for Assessment of Positive Symptoms, Scale for Assessment of Negative Symptoms, Tool for Recognition of Emotions in Neuropsychiatric Disorders, Toronto Alexithymia Scale, Metacognitive Assessment Scale, self-reflectiveness subscale of Beck's Cognitive Insight Scale, Scale S and Scale U subscales of the Metacognitive Assessment Scale, and Groningen's Social Dysfunction Scale. RESULTS AND CONCLUSION: = 0.05, df = 51). There was no significant correlation between emotion recognition and metacognition in the clinical group. The presence of negative symptoms was significantly associated with social functioning in persons with schizophrenia. KEY MESSAGES: Clinical symptoms, in particular negative symptoms, play an important role in social functioning in persons with schizophrenia and it is necessary to address these along with social cognition in order to improve functioning.
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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 | 0.002 |
| 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".