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Record W2054572064 · doi:10.1177/0829573512468847

A Snapshot of School-Based Mental Health and Substance Abuse in Canada

2013· article· en· W2054572064 on OpenAlexaffabout
Ian Manion, Kathy Short, Bruce Ferguson

Bibliographic record

VenueCanadian Journal of School Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsHospital for Sick ChildrenMental Health Research CanadaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMental healthSubstance abuseCommissionSubstance abuse preventionBest practicePsychologySubstance useMedical educationPsychiatryMedicinePolitical science

Abstract

fetched live from OpenAlex

The Mental Health Commission of Canada supported a comprehensive research project to determine the current state of mental health and substance use programs and practices in Canadian schools. The School-Based Mental Health and Substance Abuse Consortium is made up of a group of 40 leading Canadian researchers, policy makers, and practitioners. The Consortium systematically reviewed literature from around the world, conducted a program scan (147 programs) of current practices in Canadian schools, and distributed a national survey to school boards ( n = 177) and schools ( n = 643) seeking input on the state of knowledge and practice in child and youth mental health and substance abuse. This information is being shared with policy makers and school boards to help inform the delivery of future mental health services in Canada’s schools.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.674

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.013
Science and technology studies0.0110.001
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.281
Teacher spread0.255 · 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

Citations17
Published2013
Admission routes2
Has abstractyes

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