Assessment of local interest in trying edible cannabis products once legalized in Ontario, and awareness of their effects: a cross-sectional survey of youth and adults aged 16 years and older in Wellington–Dufferin–Guelph in 2018
Bibliographic record
Abstract
Objectives To assess (i) local interest in trying cannabis edibles once legal, (ii) awareness of the delayed onset of effects of edibles, and (iii) to identify characteristics associated with interest in trying edibles. Method(s) An anonymous, online cross-sectional survey was conducted (2018) and included questions on recreational cannabis use, respondent demographics, and questions specific to edibles. Descriptive analyses and multinomial logistic regression modelling were conducted to identify characteristics associated with interest in trying edibles. Results There were 3013 eligible responses. Over half of respondents indicated interest in trying edibles, including many who never used cannabis previously. Many respondents intended to prepare edibles at home. Over a third of respondents underestimated the time to onset of effects of edibles. The following variables increased the odds of a respondent being interested in trying edibles: area of residence, cannabis usage status, sex, age group, employment status, education level and awareness of the Lower Risk Cannabis Use Guidelines and of time to effect onset. Conclusion Educational messaging should target those most likely to use edibles and address potential knowledge gaps concerning time to onset of effects and safe preparation of edibles at home. Pre-prepared edibles labelling should include information on serving size and anticipated time to onset of effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".