Violence in families providing care to relatives with schizophrenia
Bibliographic record
Abstract
The aim of this dissertation was to identify factors that contribute to violence in families who provide care to relatives with schizophrenia, based on a proposed theoretical model of pathways to violence in such families. The proposed theoretical model was established by integrating family factors of expressed emotion and caregiver distress that are associated with psychiatric relapse, and illness factors of schizophrenia of insight into the illness, substance abuse problems and threat/control-override symptoms that are associated with risk of violence among these persons, with existing models of violence and mental illness. The study employed a survey design with a convenience sample of 61 caregivers who lived with and provided care to relatives with schizophrenia in the last 12 months before the survey. Twenty-six percent and 31% of caregivers experienced at least one incident of severe physical assault or one incident of minor physical assault respectively, in the past 12 months. Forty-four percent and 64% of caregivers experienced at least one incident of severe psychological aggression or one incident of minor psychological aggression respectively, during the same period. Multiple regression analysis showed that Threat/Control-Override symptoms in relatives with schizophrenia was the only significant predictor of physical assault against caregivers. Critical Comments of the caregivers and Threat/Control-Override symptoms in relatives with schizophrenia were significant predictors of psychological aggression against caregivers. The results supported the recommendation for clinicians to collaborate with caregivers in the treatment plan to reduce the intensity of the symptoms and the risk of violence in the family environment. The results also supported the recommendation to formally recognize and respect caregivers as partners in the Mental Health Act of Ontario to strengthen the collaboration between mental health professionals and caregivers for better treatment outcomes in providing care to their relatives with schizophrenia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".