Analyse des tendances de la prévalence de la consommation de cannabis et des mesures connexes au Canada
Bibliographic record
Abstract
L'administration fédérale canadienne a légalisé la consommation du cannabis à des fins non médicales en octobre 2018. Une surveillance continue de l'incidence de ce changement est nécessaire puisque de l'incertitude persiste quant aux répercussions de la légalisation sur les comportements de consommation de cannabis et au fait de savoir si ces répercussions toucheront certains plus que d'autres. L'étude s'appuie sur des données de l'Enquête canadienne sur l'alcool et les drogues et celle qui l'a précédée pour examiner à plus long terme des taux historiques de consommation et de consommation quotidienne pendant la période de 2004 à 2017. Cinq cycles de l'Enquête nationale sur le cannabis menés en 2018 et en 2019 ont été utilisés pour étudier la consommation de cannabis (totale, quotidienne ou quasi quotidienne, quantité et types de produits) dans les mois précédant et suivant la légalisation.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".