Typologies of cannabis users and associated characteristics relevant for public health: a latent class analysis of data from a nationally representative Canadian adult survey
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
Cannabis is the most prevalently used illicit drug in Canada. Current policy consists primarily of universal use prohibition rather than interventions targeting specific risks and harms relevant for public health. This study aimed to identify distinct groups of cannabis users based on defined use characteristics in the Canadian population, and examine the emerging groups' associations with differential risk and harm outcomes. One thousand three hundred and three current (i.e. use in the past three months) cannabis users, based on data from the representative cross-sectional 2004 Canadian Addiction Survey (N = 13,909), were statistically assessed by a 'latent class analysis' (LCA). Emerging classes were examined for differential associations with socio-demographic, health and behavioral indicators on the basis of chi-square and analysis of variance techniques. Four distinct classes based on use patterns were identified. The class featuring earliest onset and highest frequency of use [22% of cannabis user sample or 2.2% (95% confidence interval (CI) = 1.8-2.7%) of the Canadian adult population] was disproportionately linked to key harms, including other illicit drug use, health problems, cannabis use and driving, and cannabis use problems. A public health framework for cannabis use is needed in Canada, meaningfully targeting effective interventions towards the minority of users experiencing elevated levels of risks and harms.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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