Experiences of Family Violence Committed by Relatives With Severe Mental Illness: A Grounded Theory
Bibliographic record
Abstract
In forensic psychiatry, family violence perpetrated by a loved one suffering from severe mental illness is a significant problem thought to affect nearly half of families. To examine this poorly documented issue, a qualitative study using a grounded theory research strategy was conducted with family members who have experienced violence committed by a relative with severe mental illness. Semistructured interviews were conducted with 14 participants who had experienced this type of violence. The works of poststructuralist thinkers Jacques Donzelot and Michel Foucault inform the theoretical framework. Qualitative analysis of the data led to the emergence of five major themes: medicolegal apparatus, experience of violence, family's responsibility toward the violent relative, exclusion and stigmatization, and suffering and resilience. The main results of this qualitative study indicate that families are governed through specific mechanisms, including instrumentalization of the family's role and transfer of the violent person's care to the family. Obstacles preventing families from being included in their relative's care were also raised. This research contributes to nursing by shedding light on clinical interventions and health policy in family care. It also offers insight into the provision of appropriate quality care in particularly complicated family situations.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".