Assessing the Prevalence of Cannabis Use Through a Survey About Criminal Activity Versus One About Alcohol, Tobacco, and Other Drugs
Bibliographic record
Abstract
Background: The prevalence of cannabis use in the United Kingdom might be underestimated using the Crime Survey of England and Wales.The current study examined whether responding to questions about their cannabis use as part of a crime survey would be less likely to report that they use cannabis compared to those responding to the same questions that are part of a survey about health.Methods: Participants were randomized to be told that the items about cannabis use came from a crime survey versus from a health survey.In addition, the sample was recruited using a representative online sampling method and compared to published rates of self-reported cannabis use collected as part of the Crime Survey for England and Wales.Results: There was no significant difference (p > 0.05) in the proportion endorsing cannabis use between those told the items came from a crime survey versus a health survey.However, self-reported rates of cannabis use collected as part of the online panel (51.3% ever use; 11.9% past year; age range 18-64 years) appeared higher than those reported based on results from the Crime Survey for England and Wales (37.2% ever and 5.8% past year; age range 18-59 years).Conclusion: The current study did not find evidence that manipulating whether participants were told that the items asking about cannabis use came from a survey asking about criminal activity versus one about health had an impact on self-reported cannabis use.However, as prevalence estimates generated by the Crime Survey of England and Wales do appear to be an underestimate of actual levels of cannabis use in the United Kingdom, further research is merited on this topic.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".