Affective psychopathology and recognition of facial expressions in schizophrenia and in affective disorders
Bibliographic record
Abstract
Introduction We noticed some differences between the patients diagnosed with schizophrenia, those diagnosed with affective disorders and the normal persons, as regards the empathy level, the recognition degree of facial expressions, depression level of positive/negative affectivity. Objectives The exploration of its affectivity and pathology, as well as the changes emerging in the forming of interpersonal relationships with others in schizophrenia and in affective disorders, and the comparison of these changes with the values recorded within the group of normal persons. Aims Highlighting some differences as regards aspects of affective life and forming of interpersonal relationships in patients diagnosed with schizophrenia/affective disorder. Methods The instruments used: Beck's Depression Inventory (BDI), Toronto Empathy Questionnaire (TEQ), The Positive and Negative Affect Schedule (PANAS) and a test of identification of facial expressions. Results Empathy level in close relation to type of psychiatric disorder ( F =26.84, P P F =9.15, P F =4.83, P =0.011). Capacity of recognition of facial expression in relation with psychiatric diagnosis ( P Conclusions The conclusions of the research highlight the changes emerging at the level of affectivity in pathology, as well as the effects these changes have over the patient's contact with his/her own emotions and with the emotions of those around.
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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 | 0.000 | 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".