Aperçu - Intégrer l’équité : examen d'interventions visant l’améliorationde la santé mentale
Bibliographic record
Abstract
Au Canada, il est difficile de trouver des exemples d’interventions positives en santé mentale auprès de la population qui respectent les normes scientifiques en matière de données probantes. Il est encore plus difficile de cerner des interventions efficaces qui traitent de l’équité en santé. La divergence entre les normes en matière de données probantes en sciences de la santé et les données effectivement tirées des expériences sociales n’est pas nouvelle. Les efforts déployés pour résoudre ces différences conduisent à une tendance en faveur des interventions contrôlées en santé publique. Cependant, il est possible d’extraire des résultats d’interventions quasi-expérimentales qui répondent aux normes scientifiques tout en ayant le potentiel d’avoir un impact positif sur l’équité en santé mentale. Cet article décrit le travail entrepris en 2015 pour commencer à combler cette lacune.
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 | 0.034 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".