Problematic substance use or problematic substance use policies?
Bibliographic record
Abstract
This special issue on substance use issues comes at a critical time for Canadian health policy makers and researchers. Most attention is currently focussed on the opioid crisis and the potential impacts of cannabis legalization. However, our most widely used and harmful substances continue to be alcohol and nicotine. Our policies to reduce harms from these substances are failing. While alcohol control policies are being gradually abandoned, opportunities to maximize the harm reduction potential of new, alternative and safer nicotine delivery devices are not being grasped. More generally, a greater focus is needed on harm reduction strategies that are informed by the experience of marginalized people with severe substance use-related problems so as to not exacerbate health inequities. In order to better inform policy responses, we recommend innovative approaches to monitoring and surveillance that maximize the use of multiple data sources, such as those used in the Canadian Substance Use Costs and Harms (CSUCH) project. Greater attention to precision in defining patterns of risky use and harms is also needed to support policies that more accurately reflect and respond to actual levels of substance use-related harm in Canadian society.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".