The Relationship between Sense of Coherence, Alexithymia, and Aggression among Psychiatric Patients
Bibliographic record
Abstract
Background: The growing research focus on aggressive behavior in individuals with psychiatric disorders is gaining significant attention, and it is essential to gain a deeper understanding of the variables linked to predicting aggression to enhance treatment and prevention strategies. Aim: The present study intends to assess the relationship between sense of coherence, alexithymia, and aggression among psychiatric patients. Subjects and Method: Utilizing a correlational research design, socio-demographic & clinical questionnaire, Sense of Coherence Questionnaire (SOC-13), Toronto Alexithymia Scale (TAS-20) and brief aggression questionnaire— (BAQ) in a sample of 200 psychiatric inpatients. Results: Most patients exhibit elevated levels of aggression and alexithymia and Low Sense of Coherence. Conclusion: There were strong statistically significant negative correlations between Sense of Coherence with both aggression and alexithymia. Notably, aggression and alexithymia were found to have a highly statistically significant positive correlation. Recommendation: A training program designed for patients with alexithymia will incorporate psychological interventions and cognitive training to help them recognize and articulate their emotions and feelings more effectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".