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Record W1721955292 · doi:10.7202/007185ar

Évaluation d’implantation dans un contexte participatif : Le processus suivi à Relais-Méthadone

2003· article· fr· W1721955292 on OpenAlexaffvenueabout
Michel Perreault, Isabelle Tremblay, Céline Mercier, David L. Barbeau, Radegonde Ndejuru, Pierre Lauzon

Bibliographic record

VenueDrogues santé et société · 2003
Typearticle
Languagefr
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité de MontréalDouglas CollegeMcGill University
Fundersnot available
KeywordsHumanitiesPolitical scienceValuation (finance)ArtBusiness

Abstract

fetched live from OpenAlex

Relais-Méthadone est un programme d’intervention qui s’inscrit dans un contexte de réduction des méfaits. Comme ce programme comporte le recours à des pratiques innovatrices à Montréal, on a jugé nécessaire d’évaluer son implantation. La collaboration entre les chercheurs, les promoteurs du projet, les responsables administratifs et les intervenants est décrite ici pour ce qui a trait à cette évaluation. On estime que l’évaluation d’implantation s’est avérée profitable pour chacune des parties. Ce succès serait attribuable aux facteurs suivants : l’intérêt pour les promoteurs d’associer la recherche au programme ; le contexte de projet pilote, qui impliquait la production de données probantes pour justifier la poursuite de ce type de programme ; le consensus entre les partenaires, y compris les bailleurs de fonds, pour procéder à une étude systématique de l’implantation du programme ; la disponibilité de financement pour l’évaluation, qui a permis aux chercheurs de s’associer dès le départ à la mise sur pied du programme ; le choix d’une démarche participative appliquée à toutes les étapes du processus, qui a favorisé l’appropriation de l’évaluation et des résultats par le milieu.

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)0.108
metaresearch head score (Gemma)0.138
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1080.138
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0050.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.179
GPT teacher head0.479
Teacher spread0.300 · 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 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

Citations5
Published2003
Admission routes3
Has abstractyes

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