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Record W1526772838 · doi:10.7202/1025909ar

La santé mentale jeunesse : un domaine à la croisée des chemins

2014· article· fr· W1526772838 on OpenAlexaffvenueabout
Cécile Rousseau, Lucie Nadeau, Annie Pontbriand, Janique Johnson‐Lafleur, Toby Measham, Joanna Broadhurst

Bibliographic record

VenueSanté mentale au Québec · 2014
Typearticle
Languagefr
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcGill UniversityCentre de Santé et de Services Sociaux de la Montagne
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

The importance of children and youth mental health is increasingly recognized. This rapidly developing field cannot be conceptualized as an extension of adult services to a younger age group and its developmental and organizational specificities are the object of debate. Reviewing recent literature in this domain and some preliminary information about the Quebec Mental Health Plan implementation, this paper addresses some of the questions which structure this debate in Quebec.Quebec mental health plan has put at the forefront collaboration among disciplines and partnership among institutions. In spite of having produced significant improvement in the field, discontinuities in services, which interfere with an ecosystemic model of care, persist. Recent studies suggest that the organisational climate which surrounds youth mental health services has a direct impact on the quality of services and on youth health outcomes. A flexible management structure, which engages clinicians and health workers, favors empowerment, minimizes work stress and facilitates partnership, is needed to foster successful interdisciplinary and intersectorial collaboration. This collaboration is the cornerstone of youth mental health services.

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)0.004
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.127
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0050.008
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0150.002

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 head0.054
GPT teacher head0.374
Teacher spread0.320 · 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

Citations9
Published2014
Admission routes3
Has abstractyes

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