Les troubles des conduites alimentaires : Du diagnostic aux traitements
Bibliographic record
Abstract
Cet ouvrage, qui fait appel a une soixantaine de specialistes canadiens et europeens − medecins, psychiatres, psychoeducateurs, nutritionnistes, kinesiologues et professeurs-chercheurs −, decrit de facon approfondie les caracteristiques cles des troubles des conduites alimentaires (TCA) en s’appuyant sur les informations les plus recentes et les donnees les plus actuelles. Il dresse un panorama exhaustif des problemes de sante mentale les plus frequemment associes aux TCA, et passe en revue l’anxiete, les obsessions ou les compulsions en plus de s’interesser a l’obesite, a l’anorexie et aux dependances de toutes sortes. Qui sont les gens les plus a risque d’etre atteints de TCA ? Les femmes, bien sur, mais aussi les hommes, les enfants, les sportifs, les victimes de maltraitance durant l’enfance, ceux qui ont une deficience intellectuelle ou des troubles du spectre de l’autisme. Dans ce livre, on examine les particularites des evaluations medicales, nutritionnelles et psychosociales et on presente en detail des interventions efficaces, allant de la therapie cognitive-comportementale a l’alimentation intuitive en passant par les therapies corporelles ou familiales centrees sur les emotions. Enfin, l’accompagnement des personnes atteintes de TCA en hopital de jour, en hospitalisation ou en externe est passe a la loupe pour offrir le portrait le plus complet a jour des ressources accessibles.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".