Problems with the Identification of ‘Problematic' Cannabis Use: Examining the Issues of Frequency, Quantity, and Drug Use Environment
Bibliographic record
Abstract
Considerable recent attention has focused on how harmful or problematic cannabis use is defined and understood in the literature and put to use in clinical practice. The aim of the current study is to review conceptual and measurement shortcomings in the identification of problematic cannabis use, drawing on the WHO ASSIST instrument for specific examples. Three issues with the current approach are debated and discussed: (1) the identification of problematic cannabis use disproportionately relies on measures of the frequency of cannabis consumption rather than the harms experienced; (2) the quantity consumed on a typical day is not considered when assessing problematic use, and (3) screening tools for problematic use employ a 'one-size-fits-all approach' and fail to reflect on the drug use context (networks and environment). Our commentary tackles each issue, with a review of relevant literature coupled with analyses of two Canadian data sources--a representative sample of the Canadian adult population and a smaller sample of adult, regular, long-term cannabis users from four Canadian cities--to further articulate each point. This article concludes with a discussion of appropriate treatment interventions and approaches to reduce cannabis-related harms, and offers suggested changes to improve the measurement of problematic cannabis use.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".