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Record W1573788296 · doi:10.7202/032452ar

Points de repère pour différencier la gestion de cas du suivi intensif dans le milieu auprès des personnes souffrant de troubles mentaux graves

2007· article· fr· W1573788296 on OpenAlexfundvenueno aff
Daniel Gélinas

Bibliographic record

VenueSanté mentale au Québec · 2007
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsnot available
FundersUniversité de Montréal
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

L'auteur propose une synthèse des éléments essentiels qui permettent de différencier la gestion de cas du suivi intensif dans le milieu pour les personnes souffrant de troubles mentaux graves. En situant le développement de ces deux approches dans leur contexte social, l'auteur identifie les points de repère qui permettent de les distinguer à la fois au plan conceptuel et pratique. Cet exercice permet de dissiper la confusion répandue dans les écrits et d'outiller les cliniciens afin qu'ils puissent identifier les modèles les plus appropriés pour répondre aux besoins de leur clientèle. Cela implique de prendre en considération les caractéristiques du système dans lequel ils interviennent car sa configuration exerce une influence considérable sur leur travail.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.029
GPT teacher head0.338
Teacher spread0.309 · 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.

Study designQualitative
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
Published2007
Admission routes2
Has abstractyes

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