Call for papers - 2020 special issue - Problematic substance use in Canada: trends and emerging issues in public health
Bibliographic record
Abstract
Substances, such as alcohol, cannabis and other drugs pose serious challenges for public health, public safety and the health and well-being of Canadians through their potential to cause dependence, illness and/or harm.With Canada continuing to face a national opioid crisis, changes to federal legislation on cannabis, significant societal costs associated with alcohol and tobacco, and growing popularity of vaping products, it is imperative that we monitor the scope and impacts of problematic substance use through a public health lens.The goal of this special issue is to provide the latest research evidence to inform the Canadian Drugs and Substances Strategy and to share timely scientific findings with decision-makers, service providers, communities and those living with or affected by problematic substance use.Health Promotion and Chronic Disease Prevention in Canada: Research, Policy and Practice is seeking relevant topical research articles that:• Characterize the current state of problematic substance use, polyuse and substance use disorders in Canada;• Examine trends or explore emerging issues in problematic use of cannabis, opioids, alcohol, tobacco and other emerging substances;• Synthesize and/or review evidence on substance-related policies and interventions in the Canadian context.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.474 | 0.239 |
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".