Aggression, Alexithymia and Sense of Coherence in a Sample of Schizophrenic Outpatients
Bibliographic record
Abstract
Schizophrenia elevates the risk for aggressive behavior, and there is a need to better understand the associated variables predicting aggression for treatment and prevention purposes. The aim of the present study is to determine the relationship between alexithymia, sense of coherence and aggressive behavior in a sample of schizophrenic outpatients. Using a correlational research design, standardized self-report questionnaires assessed aggression (brief aggression questionnaire—BAQ), alexithymia (Toronto Alexithymia Scale—TAS) and sense of coherence (sense of coherence questionnaire—SOC) in a sample of 100 schizophrenic outpatients in clinical remission. Participants reported high levels of aggression and alexithymia along with reduced sense of coherence. Significant negative correlations were evidenced among scores on the SOC scale (p < 0.001) with both the TAS as well as with the BAQ scales. However, a positive correlation (p < 0.001) was observed between the TAS and BAQ scales. Regression indicated that 27% of the variation in the BAQ rating was explained by the TAS, while an additional 17.8% was explained by the sense of coherence. The difficulty identifying feelings of alexithymia and the comprehensibility and manageability components of sense of coherence significantly predicted anger, hostility and physical aggression. Sense of coherence mediated the relationship between alexithymia and aggression. From the path analysis, comprehensibility emerged as the key factor counterbalancing alexithymic traits and aggressive behaviors, and manageability effectuated higher anger control. The findings hold practical implications for the treatment and rehabilitation of schizophrenic patients.
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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 | 0.001 | 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) | 0.001 | 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".