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Record W2793219687 · doi:10.1177/0706743718758968

Youth Mental Health Should Be a Top Priority for Health Care in Canada

2018· article· en· W2793219687 on OpenAlexaffvenueabout
Ashok Malla, Jai Shah, Srividya N. Iyer, Patricia Boksa, Ridha Joober, Neil Andersson, Shalini Lal, Rebecca Fuhrer

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversité de MontréalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsMental healthIndigenousDiversity (politics)Health carePsychiatryPsychologyMedicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

In this article we have provided a perspective on the importance and value of youth mental health services for society and argued that advancing youth mental health services should be the number one priority of health services in Canada. Using the age period of 12-25 years for defining youth, we have provided justification for our position based on scientific evidence derived from clinical, epidemiological and neurodevelopmental studies. We have highlighted the early onset of most mental disorders and substance abuse as well as their persistence into later adulthood, the long delays experienced by most help seekers and the consequence of such delays for young people and for society in general. We have also provided a brief review of the current gross inadequacies in access and quality of care available in Canada. We have argued for the need for a different conceptual framework of youth mental disorders as well as for a transformation of the way services are provided in order not only to reduce the unmet needs but also to allow a more meaningful exploration of the nature of such problems presenting in youth and the best way to treat them. We have offered some ideas based on previous work completed in this field as well as current initiatives in Canada and elsewhere. Any transformation of youth mental health services in Canada must take into consideration the significant geographic, cultural and political diversity across the provinces, territories and indigenous peoples across this country.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.713

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0130.004
Scholarly communication0.0070.002
Open science0.0020.005
Research integrity0.0030.008
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 head0.083
GPT teacher head0.414
Teacher spread0.332 · 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 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

Citations233
Published2018
Admission routes3
Has abstractyes

Explore more

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