Relationship of aggressive behavior and depressive symptoms in paranoid schizophrenia patients.
Bibliographic record
Abstract
Introduction. The problem of depression among patients with schizophrenia is actual question in the context of re-adaptation in social community and improving of the quality of life. Stigma, isolation and lack of social support reinforces the statement of that schizophrenia patients are aggressive. Not sufficiently investigated the interdependence of depressive symptoms and aggressive behavior in these patients.Objectives. Investigate relationship of depressive symptoms and aggressive behavior among paranoid schizophrenia patients. Methods. We examined 81 paranoid schizophrenia patients: 44 u2013 with depressive symptoms and 37 u2013 without it. Our investigation is based on a complex examination included: PANSS scale, The Calgary depression scale, RFL scale, Buss-Durkey Inventory and Boyko's Definition of integral forms of communicative aggression inventory. Results. Patients of both groups showed no differences in demonstration of oral or physical aggression. Also, inability to switch aggression on to inanimate objects was high in both groups. Differences were identified in self-aggressive subscale and were statistically significantly higher in group of patients with depressive symptoms. In this group also identified higher level of spontaneity of aggression and disability to stall it. These patients showed statistically significantly lower score of RFL scale with prevalence subscales: responsibility to family, child-related concerns, moral objections.Conclusions. Results showed that depressive symptoms facilitate increasing of the level of self-aggression among paranoid schizophrenia patients. Psychotherapeutic correction should be based on existing family values and aimed at strengthening of moral principles and belief in life. This direction requires further research and development of the effective methods of medical and psychotherapeutic correction.Disclosure of interest. The authors have not supplied a conflict of interest statement.
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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) | 0.001 | 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".