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Record W2135799773 · doi:10.7202/045424ar

Famille et traitement de la toxicomanie chez les adolescents : étude de cas

2011· article· fr· W2135799773 on OpenAlexaffvenue
Benoit R. Gauthier, Karine Bertrand, Pierre Nolin

Bibliographic record

VenueEnfances Familles Générations · 2011
Typearticle
Languagefr
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

L’importance d’impliquer la famille dans le traitement de réadaptation en toxicomanie chez les adolescents fait consensus, mais la nature de son influence demeure peu documentée. Objectifs. Le but de cette étude de cas est de mieux comprendre la contribution de l’implication familiale dans ce processus de réadaptation. Méthode. Des entrevues individuelles semi-structurées ont été menées auprès de deux jeunes en traitement de la toxicomanie, leurs parents et les cliniciens les ayant suivis. Des données quantitatives complémentaires pré et post-traitement sur la gravité des problèmes familiaux et de consommation auprès de deux jeunes ont été recueillies. Résultats. Les récits de ces différents acteurs soulignent les bienfaits d’une implication parentale constante à travers les différentes étapes du traitement.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.318
Teacher spread0.281 · 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 designCase report
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

Citations3
Published2011
Admission routes2
Has abstractyes

Explore more

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