Problems with the Identification of ‘Problematic' Cannabis Use: Examining the Issues of Frequency, Quantity, and Drug Use Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it