Utilité des mesures standardisées dans la recherche et la clinique familiales : les échelles d’auto-évaluation familiale
Bibliographic record
Abstract
L’utilité de la méthode d’auto-évaluation familiale a longtemps été contestée dans la recherche et la clinique familiale du fait de son manque d’esprit systémique et de l’absence de concordance entre les données issues des perspectives d’auto-évaluation et d’hétéro-évaluation, la seconde approche étant généralement considérée comme plus complète et valide que la première. L’objectif de cet article est de mettre en valeur l’intérêt spécifique de la méthode d’auto-évaluation en présentant plusieurs auto-questionnaires de fonctionnement familial, leurs utilisations précises et leurs retombées pour la clinique et la recherche. Une meilleure acceptation de cette approche évaluative contribuera aussi à un rapprochement entre la recherche et la pratique clinique.
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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 | 0.191 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".