Validation québécoise et élaboration de la forme auto-révélée du Health-Sickness Rating Scale
Bibliographic record
Abstract
Le Health-Sickness Rating Scale (HSRS; Luborsky, 1962) permet d’évaluer empiriquement la santé mentale. Plusieurs recherches ont permit d’établir ses qualités psychométriques (Armelius, Gerin, Luborsky & Alexander, 1991; Luborsky, 1962, 1975;Luborsky & Bachrach, 1974; Harty, Cerney, Colson, Coyne, Frieswyk, Johnson &Mortimer, 1981). Cette recherche se divise en deux parties. Dans la première, elle vise une traduction du HSRS, une évaluation de sa validité et de sa fidélité interjuges auprès d’intervenants québécois en santé mentale ainsi qu’une nouvelle validation internationale de l’instrument. Trente et un professionnels ont participé à cette partie de l’étude. Les résultats montrent une fidélité interjuges forte (ICC de 0.78) entre l’ensemble des intervenants québécois, une validité concurrente très forte (ICC de 0.97). Le nouvel indice de validité internationale calculé auprès des professionnels de quatre pays (Etats-Unis, France, Québec et Suisse) est également très fort (ICC de 0.98). La deuxième partie de cette recherche vise l’élaboration et la validation la forme auto-révélée du HSRS. Trente-cinq sujets ont participé à l’étude. Les résultats indiquent une corrélation forte (ICC de 0.79) entre la forme auto-révélée du HSRS et la version originale. La fidélité interjuges est également élevée (ICC de 0.83).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".