Help-seeking behaviours among cannabis consumers in Canada and the United States: Findings from the international cannabis policy study
Bibliographic record
Abstract
Background: Little literature exists on what sources of help individuals utilize for cannabis-related problems. The current study examined the percentage of consumers who sought help to manage cannabis-related problems, such as perceived cannabis use disorder, the most common sources of help sought, and factors associated with help-seeking. Methods: = 13,209) completed an online survey from the International Cannabis Policy Study. Past 3-month help-seeking behaviours, respondent's perceived addiction to cannabis, legal status of cannabis in their jurisdiction, and risky behaviours associated with cannabis use was assessed. Results: < .001). In comparison to consumers in Canada and 'legal' US states, respondents in 'illegal' US states were more likely to seek help from family and friends (Canada: AOR = 5.73, 2.21-14.91; US: AOR = 4.76, 2.00-11.11) and less likely to seek help from a doctor/physician (Canada: AOR = 0.46, 0.24-0.90; US: AOR = 0.51, 0.27-0.99). Conclusion: Roughly 1 in 10 cannabis consumers sought help from a range of sources, including a third who are at high risk of problematic use. More informal sources of help, such as seeking help from online sources are frequently used. Future research should examine these frontline sources of help for cannabis consumers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".