Bibliographic record
Abstract
Cet article a pour objectif de démontrer l'importance qu'a acquise la problématique de l'errance en santé mentale au cours des trente dernières années, et de proposer des repères pour le développement de pratiques accordées aux conditions de cette réalité. L'auteur procède à une interprétation du contexte sociohistorique en santé mentale qu'il décrit comme un déplacement à 180° du risque de l'enfermement institutionnel à l'enfermement dans l'errance. Au plan psychosocial, il propose une compréhension de l'errance comme une impuissance vécue de la liberté. Ensuite, à partir d'une relecture de son expérience en tant que clinicien et responsable d'organisme, il présente trois axes de développement des services : l'accueil dans un contexte d'urgence sociale, l'accompagnement continu et l'amélioration des conditions de vie et de la participation sociale.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".