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Record W2321690866 · doi:10.1017/ipm.2014.89

Transforming youth mental health: a Canadian perspective

2015· article· en· W2321690866 on OpenAlexaffabout
Srividya N. Iyer, Patricia Boksa, Shalini Lal, Jai Shah, Gina Marandola, Gerald Jordan, Madeline Doyle, Ridha Joober, Ashok Malla

Bibliographic record

VenueIrish Journal of Psychological Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversité de MontréalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsMental healthScope (computer science)IndigenousScale (ratio)Mental healthcareMental health servicePerspective (graphical)Service (business)National Service FrameworkPsychologyHealth carePublic relationsPolitical scienceMedicineNursingPsychiatryBusinessGeography

Abstract

fetched live from OpenAlex

In most mental illnesses, onset occurs before the age of 25 and the earliest stages are critical. The youth bear a large share of the burden of disease associated with mental illnesses. Yet, Canadian youths with mental health difficulties face delayed detection; long waiting lists; inaccessible, unengaging services; abrupt transitions between services; and, especially in remoter regions, even a complete lack of services. Responding to this crisis, the Canadian Institutes of Health Research announced a 5-year grant that was awarded to ACCESS, a pan-Canadian network of youths, families, clinicians, researchers, policymakers, community organisations and Indigenous communities. Using strategies developed collaboratively by all stakeholders, ACCESS will execute a youth mental healthcare transformation via early detection, rapid access and appropriate, high-quality care. The project includes an innovative, mixed-methods service research component. Similar in many respects to other national youth mental health initiatives, ACCESS also exhibits important differences of scale, scope and approach.

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)0.003
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.619
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.391
GPT teacher head0.547
Teacher spread0.156 · 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

Citations59
Published2015
Admission routes2
Has abstractyes

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