Bibliographic record
Abstract
Cet article examine la stigmatisation et ses conséquences sur les personnes utilisatrices de services en santé mentale. L’auteur présente les résultats de trois programmes d’envergure sur la réduction de la stigmatisation implantés aux niveaux national et international. L’accent est mis sur des expériences du programme pilote canadien de l’Organisation mondiale de la santé de lutte contre la stigmatisation et la discrimination pour cause de schizophrénie (Canadian Pilot Program of the World Health Organization’s Global Program to Fight Stigma and Discrimination Because of Schizophrenia). On en tire des leçons pour de meilleures pratiques en matière de programmes anti-stigmatisation. À ce jour, les expériences suggèrent que les interventions les moins coûteuses sont probablement celles qui visent des sous-populations spécifiques. Des campagnes d’éducation populaire à grande échelle ont été décevantes et ne semblent pas produire de changements significatifs d’attitudes et de comportements. Les approches qui privilégient les contacts avec les personnes utilisatrices de services en santé mentale dans un contexte d’éducation anti-stigmatisation s’avèrent plus prometteuses.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".