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Record W4405281030 · doi:10.7202/1114903ar

Validation du Kessler-10, Kessler-6 et du PHQ-9 auprès de la population générale québécoise francophone

2024· article· fr· W4405281030 on OpenAlexaffvenue
Geoffrey Gauvin, Louis‐Philippe Côté, Cécile Bardon

Bibliographic record

VenueRevue québécoise de psychologie · 2024
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Cette étude vise à évaluer les propriétés psychométriques de l’échelle de détresse psychologique de Kessler (K10 et K6) et du questionnaire sur la santé du patient (PHQ-9), auprès de la population générale québécoise francophone pour deux groupes d’âge. Un total de 1 954 participants ont rempli un questionnaire en ligne comprenant le K10, le K6, le PHQ-9 et diverses échelles en santé mentale. Des analyses factorielles exploratoires et confirmatoires ont permis d’examiner leurs structures factorielles auprès de la population générale québécoise (jeunes : 14−24 ans; adules : 25−64 ans). La cohérence interne et les analyses de convergence se révélaient satisfaisantes pour les deux groupes d’âge. Ces résultats justifient l’utilisation du K10, K6 et PHQ-9 dans la population générale québécoise.

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.397
Teacher spread0.353 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes2
Has abstractyes

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