The Lower-Risk Cannabis Use Guidelines (LRCUG): A ready-made targeted prevention tool for cannabis in New Zealand
Bibliographic record
Abstract
Cannabis use is common, especially among young people, and associated with risks for select acute and chronic adverse health and social outcomes. New Zealand features overall high cannabis use levels, yet may soon follow other jurisdictions and implement legalization of non-medical cannabis use and supply towards public health objectives. While existing cannabis-oriented interventions mainly focus on primary prevention and treatment (e.g., for dependence), key harms from use are crucially influenced by risk factors that can be modified by the user. On this basis, and similar to other health behavior-oriented interventions, 'Lower-Risk Cannabis Use Guidelines' (LRCUG), consisting of 10 recommendation clusters for lower-risk use, were systematically developed in Canada as an evidence-based, targeted prevention tool towards reducing adverse outcomes among cannabis users. We briefly summarize the concept of and experiences with implementation of the LRCUG elsewhere, and describe how their adoption as a population health intervention may serve public health goals of possible cannabis legalization in New Zealand and elsewhere.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".