Le « bloc » Psy Cause Canada au cinquantième congrès de l’AMPO à Mont Tremblant le 3 juin 2016
Bibliographic record
Abstract
Le présent article vise la traduction et la validation canadienne-française du Global Appraisal of Individual Needs - Short Screener (GAIN-SS). La version française a été intitulée échelle Globale des Besoins Individuels - Dépistage bief (EGBT-DBj. Le ptoctssui; de traduction du GAIN-SS a été effectué selon une procédure multi-méthodes : (a) traduction/ retraduction, (b) traduction de type comité, (c) étude de la validité apparene, (d) validation sommaire d’une version bilingue de l’instrument et finalement, traduction et adaptation du matériel complémentaire. Le processus de validation empirique de l’instrument a été effectué à l’aide à quatre étude permettant une évaluation : (a) de la validité de construit, (b) de la fidélité temporelle et (c) la validation concomitante et divergeante. En conclusion, il semble que l’EGBI-DB possède de bonnes propriétés psychométriques en termes de fidélité et de la validité. Par conséquent, les franco-ontatiens et plus largement les francophones peuvent désormais bénéficier d’un outil de dépistage des troubles concomitants disponible dans leur langue et validé empiriquement.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".