The International Cannabis Toolkit (iCannToolkit): a multidisciplinary expert consensus on minimum standards for measuring cannabis use
Bibliographic record
Abstract
BACKGROUND: The lack of an agreed international minimum approach to measuring cannabis use hinders the integration of multidisciplinary evidence on the psychosocial, neurocognitive, clinical and public health consequences of cannabis use. METHODS: A group of 25 international expert cannabis researchers convened to discuss a multidisciplinary framework for minimum standards to measure cannabis use globally in diverse settings. RESULTS: The expert-based consensus agreed upon a three-layered hierarchical framework. Each layer-universal measures, detailed self-report and biological measures-reflected different research priorities and minimum standards, costs and ease of implementation. Additional work is needed to develop valid and precise assessments. CONCLUSIONS: Consistent use of the proposed framework across research, public health, clinical practice and medical settings would facilitate harmonisation of international evidence on cannabis consumption, related harms and approaches to their mitigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".