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Record W2972013286 · doi:10.7202/1061727ar

Validation d’un tri de cartes Q pour l’évaluation de l’adaptation sociale en psychiatrie

2019· article· fr· W2972013286 on OpenAlexaffvenue
Robert Groleau, Marc Bigras, Gilles Côté

Bibliographic record

VenueRevue de psychoéducation · 2019
Typearticle
Languagefr
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec à MontréalInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Cette étude présente la validation d’un instrument d’évaluation de l’adaptation sociale en psychiatrie (Q-ASP). Cet instrument utilise la méthode du tri de cartes Q, car elle présente plusieurs avantages pour les cliniciens qui travaillent en situation de vécu partagé et d’observation participante. Elle permet de construire un instrument qui s’adapte au contexte de pratique de ces professionnels en fournissant des données qualitatives et quantitatives. L’instrument permet une évaluation valide de l’adaptation sociale dans un contexte de réadaptation psychiatrique de patients qui présentent un risque de violence et de toxicomanie. La méthodologie repose sur l’évaluation de 33 patients. Les résultats liés à la validité du Q-ASP démontrent un bon indice de fidélité interjuges (r=0,70), une excellente stabilité (r=0,95) et une bonne validité de convergence avec le HoNOS (r=0,62). Des experts ont pu s’entendre (r=0,81) pour décrire un profil optimal d’un patient suffisamment adapté pour vivre dans la communauté. Le principal avantage de cet instrument est qu’il permet de dresser un profil comportemental qui identifie les facteurs de risque et de protection liés à l’adaptation sociale de patients psychiatriques.

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)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.388
GPT teacher head0.600
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations1
Published2019
Admission routes2
Has abstractyes

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