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Record W7135000841

Toronto's Youth Mental Health Court: Built on Research Evidence

2023· other· en· W7135000841 on OpenAlexaboutno aff
Lauren Goldberg, Michele Peterson-Badali, Aminah Chambers, Tracey A. Skilling

Bibliographic record

VenueTSpace · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthMental illnessSuicide preventionMental health lawOccupational safety and healthPoison control
DOInot available

Abstract

fetched live from OpenAlex

Over the past 20 years, youth mental health courts have been established across North America to address the high prevalence of mental health needs among adolescents who appear in juvenile and youth criminal courts. Toronto’s Community Youth Court was created in 2011 to improve timely access to community treatment services and youth well-being and reduce case-processing time and recidivism, with research and evaluation built in from its inception. In this brief we describe the court’s structure and operations in relation to research evidence on core characteristics of effective mental health court programs: collaboration between community agencies and specialized court teams to develop and manage treatment plans, the use of reliable and valid mental health screening and/or assessment tools to establish needs, and timely connection to appropriate mental health services. We discuss court outcomes, challenges encountered, and how research findings can guide future practice.

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)0.011
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.293
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.003
Scholarly communication0.0070.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.001

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 head0.361
GPT teacher head0.552
Teacher spread0.191 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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