At the Interface of Justice and Mental Health: Key Actors’ Perceptions of Involuntary Treatment Orders in Quebec (Canada)
Bibliographic record
Abstract
Involuntary treatment orders can be used to compel individuals deemed incompetent to receive care that is considered necessary for their health condition. This article describes the varying perceptions of the legal procedures associated with involuntary treatment orders. Semi-structured interviews were conducted with 40 participants (service users, family members, healthcare professionals, and lawyers). The overarching theme “at the intersection of legal and clinical issues” captures the ways in which involuntary treatment orders are experienced and used at the crossroads of two distinct systems. Three interrelated themes were identified. The first theme, The Construction and Negotiation of Involuntary Treatment Orders , examines how involuntary treatment orders are shaped through formal legal criteria, informal clinical reasoning, institutional norms, and strategic negotiation, often revealing tensions and overlaps between legal and clinical logics. The second theme, Relational Dynamics in the Initiation of Involuntary Treatment Orders , focuses on how interpersonal interactions among different key actors influence perceptions of fairness, legitimacy, and coercion in the process surrounding involuntary treatment orders. The third theme, Barriers and Inequities in Access to Justice , highlights how structural, procedural, and psychosocial factors constrain service users’ ability to exercise their rights and shape unequal experiences within the legal process.
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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 | 0.001 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".