Roles of Families in the Forensic Mental Health System in Ontario, Canada: An Interpretive Phenomenology
Bibliographic record
Abstract
The study aims to explore the roles held by family members in the forensic mental health system, focusing on how these roles are embodied, performed and adhered to, as well as understanding the intersecting and sometimes conflicting nature of these roles. Employing a qualitative methodology, specifically interpretive phenomenology, and drawing on the theoretical framework of Jacques Donzelot regarding the roles of families as socio-political structures, this research design enables the exploration of family experiences within the realm of the forensic mental health system. Semi-structured interviews with 17 family members of persons in the forensic mental health system in Ontario, Canada, were conducted. The interviews were transcribed verbatim and analysed using a three-pronged approach aligned with interpretive phenomenology principles, including the spatial, temporal and interpersonal dimensions of lived experiences. Findings indicate that family members of relatives in the forensic mental health system embody many roles, which can be grouped into four categories: security guard and jailer, ‘partner’ in care, advocate and family member/friend. The study concludes that families may experience intrapersonal, interpersonal and intra-familial tension caused by the contradicting roles family members must take on when interacting with the forensic mental health system.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".