Utility of risky cannabis use concept and the role of standard units for achieving an operational definition
Bibliographic record
Abstract
Abstract Over the past decade (2010-2019), the number of people admitting to using cannabis in the European Union (including the United Kingdom, Norway, and Turkey) increased by 27%, from 3.1% to 3.9%. Notably, Portugal, Spain, and Luxembourg topped the list with the highest percentages of daily cannabis users among those who had consumed the substance in the last month. With the relaxation of recreational cannabis laws in various European countries, such as Germany, Malta, and Luxembourg, there is a growing need for a public health-oriented and preventative approach. Drawing parallels with alcohol-related strategies, this session aims to explore this evolving landscape from a clinical perspective. The focus will be on the World Health Organization’s definition of risky substance use, aiming to make it practical and applicable. Two existing proposals from Canada and Spain will be reviewed, with an emphasis on the role of standardized cannabis units in defining risk and the quest for consensus in this regard. Additionally, the session will examine the similarities between alcohol and cannabis consumption, looking at the effectiveness of the Standard Drink Unit in early intervention and prevention of alcohol-related problems. Insights from the alcohol domain will be discussed, offering valuable lessons for preventing cannabis-related harm. Disclosure of Interest None Declared
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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 | 0.032 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".