Major challenges in substance use research in Canada in 2019
Bibliographic record
Abstract
Aims: To synthesize knowledge on substance use and substance-attributable burden in Canada to determine research priorities for the next 3 to 5 years. Methods: We searched for and analyzed the latest epidemiological estimates of substance use prevalence and attributable burden and for economic data on the costs of substance use. Results: Based on trends over 2014-2019, opioid, alcohol, and cannabis use were identified as research priorities due to their current or anticipated future impact on health burden in Canada. Specifically, future research efforts should be directed towards: (a) reducing the number of opioid prescriptions, investing in interventions for those already addicted to opioids, preventing both the development of opioid use disorders and deaths due to overdose; (b) identifying ways to reduce hazardous and harmful drinking, particularly among those with low socioeconomic status; and (c) monitoring and evaluating the impacts of the recent policy implementations for the legalization of cannabis on various outcomes. While tobacco attributable burden has been decreasing, it is important to continue to monitor vaping use over time. Conclusions: Substance use is a significant and increasing risk factor for burden of disease, and research efforts are necessary to reduce this burden in Canada.
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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 | 0.091 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| 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".