MétaCan
Menu
Back to cohort
Record W4416786474 · doi:10.1016/j.ijlp.2025.102166

Lack of challenge to substantive criteria at mental health tribunals: Amplifying the medical perspective?

2025· article· en· W4416786474 on OpenAlexafffundabout
Sam Boyle, Fiona Jäger, Jean‐Laurent Domingue, Amélie Perron

Bibliographic record

VenueInternational Journal of Law and Psychiatry · 2025
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of OttawaSt. Lawrence College
FundersSaint Lawrence College
KeywordsTribunalMental healthPerspective (graphical)Subject (documents)Mental health lawOrder (exchange)Medical treatmentInformed consent

Abstract

fetched live from OpenAlex

Mental health tribunals provide legal safeguards for compulsory treatment for mental illness. Despite one of these tribunals' purposes being to give people subject to compulsory treatment a "day in court", research has shown that individuals' experience of mental health tribunals is highly negative. To understand these negative experiences, we conducted a multi-stakeholder study of the Consent and Capacity Board, a mental health tribunal in Ontario, Canada. Our research revealed that disputes in the hearings tended to focus on procedural requirements of the compulsory treatment orders, and although substantive legal criteria were addressed, the medical conclusions underlying those criteria were not directly challenged. Further, in cases where a client "wins" and the treatment order is revoked, our analysis shows the medical perspective remained authoritative. Finally, although people subject to treatment orders were given a chance to speak at hearings, in most cases theirs was the only voice challenging the psychiatrist's medical conclusions, and their contribution would usually only lessen the chance of the order being revoked. Therefore, we argue that rather than challenging medical decision-making, as may be expected, tribunal hearings unintentionally amplify the medical perspective in a manner that is likely to be upsetting for people subject to the treatment orders. We acknowledge that this effect is ingrained in the current system and will be challenging to ameliorate. Nonetheless, it is an important consideration for those in legal and clinical practice, and policy makers. We give some suggestions about how these experiences could be improved and for further research opportunities.

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 imitation

Not 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.

metaresearch head score (Codex)0.059
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.114
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0220.051
Scholarly communication0.0240.017
Open science0.0040.013
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.073
GPT teacher head0.500
Teacher spread0.426 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Law and PsychiatrySame topicHealthcare Decision-Making and RestraintsFrench-language works237,207