Methods to Assess Cannabis Consumption in Population Surveys: Results of Cognitive Interviewing
Bibliographic record
Abstract
The Cannabis Act legalized the possession and sale of nonmedical cannabis in Canada on October 17, 2018. Evaluating the impact of cannabis legalization requires a more thorough understanding than is provided by most existing measures of cannabis use. The aim of this study was to pretest a range of cannabis consumption measures used in a population-based survey and to share insights gained in the process. Cognitive interviewing was conducted among 10 cannabis users aged ≥16 years. Comprehension and self-reporting of consumption types and amounts, sources of purchase, and cannabinoid levels were examined. Findings revealed areas for improvement in a number of survey items, including unclear wording and reference images. Identified issues were used to improve the survey for use in the International Cannabis Policy Study. The authors discuss important principles (e.g., use of visual cues, user-selected units, and time frames) that should be adopted when assessing cannabis use in population-based studies.
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How this classification was reachedexpand
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 | 0.081 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".