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Record W4296719230 · doi:10.25159/2522-3062/9428

Considering Mental Health Courts for South Africa: Lessons from Canada and the United States of America

2022· article· en· W4296719230 on OpenAlexaboutno aff
Letitia Pienaar

Bibliographic record

VenueComparative and International Law Journal of Southern Africa · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthCriminal justiceLegislatureTherapeutic jurisprudenceEconomic JusticeMental illnessLawPolitical scienceMental health lawHealth careCriminologyMedicinePsychiatryPsychology

Abstract

fetched live from OpenAlex

Under the South African criminal justice system, mentally ill persons in conflict with the law spend long periods awaiting forensic assessment, owing to resource shortages (staff and available beds). Accused persons awaiting forensic assessment are often kept in correctional facilities where mental health care services are lacking. This leaves many accused individuals who are mentally ill at risk of falling between the proverbial cracks of the system. The diversion of the accused with mental illness from the criminal justice system into a treatment programme could address this problem. Currently no such formal diversion option exists in South Africa. Mental health courts as a formal diversion option are gaining popularity in jurisdictions such as Canada and the United States of America, where delays with forensic assessments and, in particular, pre-trial fitness assessments are rife. These courts employ therapeutic jurisprudence to deliver justice. This contribution explores the nature of a mental health court and looks at such courts in Canada and the United States of America and considers whether South Africa could benefit from such a court and whether it would be viable within the South African legislative framework.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.334
Teacher spread0.258 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

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