Violence by Persons with Serious Mental Illness Toward Family Caregivers and Other Relatives: A Review
Bibliographic record
Abstract
ABSTRACT: Persons living with serious mental illness (SMI) are at a modestly increased risk of committing violence and are disproportionately likely to target family members when they do commit violence. In this article, we review available evidence regarding violence by persons with SMI toward family members, many of whom are caregivers. Evidence suggests that a sizable minority of family members with high levels of contact with persons with SMI have experienced violence, with most studies finding rates of past year victimization to be 20% or higher. Notable risk factors for family violence pertaining specifically to persons with SMI include substance use, nonadherence to medications and mental health treatment, history of violent behavior, and recent victimization. Notable risk factors pertaining specifically to the relationships between persons with SMI and family members include persons with SMI relying on family members for tangible and instrumental support, family members setting limits, and the presence of criticism, hostility, and verbal aggression. As described in qualitative studies, family members often perceive violence to be connected to psychiatric symptoms and inadequate treatment experiences. We argue that promising strategies for preventing violence by persons with SMI toward family members include (1) better engaging persons with SMI in treatment, through offering more recovery-oriented care, (2) strengthening support services for persons with SMI that could reduce reliance on family members, and (3) supporting the capabilities of family members to prevent and manage family conflict. The available interventions that may be effective in this context include McFarlane's Multifamily Group intervention and the Family-to-Family educational program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".