Points de repère pour différencier la gestion de cas du suivi intensif dans le milieu auprès des personnes souffrant de troubles mentaux graves
Bibliographic record
Abstract
L'auteur propose une synthèse des éléments essentiels qui permettent de différencier la gestion de cas du suivi intensif dans le milieu pour les personnes souffrant de troubles mentaux graves. En situant le développement de ces deux approches dans leur contexte social, l'auteur identifie les points de repère qui permettent de les distinguer à la fois au plan conceptuel et pratique. Cet exercice permet de dissiper la confusion répandue dans les écrits et d'outiller les cliniciens afin qu'ils puissent identifier les modèles les plus appropriés pour répondre aux besoins de leur clientèle. Cela implique de prendre en considération les caractéristiques du système dans lequel ils interviennent car sa configuration exerce une influence considérable sur leur travail.
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 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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".