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Record W1516564575 · doi:10.7202/017808ar

La création d’une nouvelle génération d’études épidémiologiques en santé mentale

2008· article· fr· W1516564575 on OpenAlexaffvenueabout
Jean Caron, Michel Tousignant, Duncan Pedersen, Marie‐Josée Fleury, Margaret Cargo, Mark Daniel, Yan Kestin, Anne G. Crocker, Michel Perreault, Alain Brunet, Jacques Tremblay, Gustavo Turecky, Serge Beaulieu

Bibliographic record

VenueSanté mentale au Québec · 2008
Typearticle
Languagefr
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de MontréalMcGill UniversityDouglas CollegeUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Grâce à une subvention des Instituts de recherche en santé du Canada (IRSC), il se développe actuellement une nouvelle génération d’études en épidémiologie sociale et psychiatrique dans une zone circonscrite se situant dans le sud-ouest de Montréal où vivent 258 000 personnes. Ce programme de recherche repose sur une étude prospective longitudinale visant à identifier les déterminants de la santé mentale de la population, et sur quatre études spécifiques qui abordent des paramètres importants pour la santé mentale : l’écologie sociale et physique des quartiers, le soutien social, le stigma social et les services en santé mentale. Ce programme est complété par l’utilisation de la dernière génération des outils technologiques et informatiques soit un système d’information géographique (SIG) qui permet d’apprécier les effets du contexte sur la santé mentale. Les bases théoriques sur lesquels repose ce modèle sont présentées de même qu’une description sommaire des méthodes utilisées.

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)0.065
metaresearch head score (Gemma)0.064
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 score0.463
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0160.014
Science and technology studies0.0040.004
Scholarly communication0.0090.005
Open science0.0040.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.002

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 head0.025
GPT teacher head0.323
Teacher spread0.298 · 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

Citations10
Published2008
Admission routes3
Has abstractyes

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