Age, period and cohort effects on time trends in monthly cannabis use in adult population: 1996–2019
Bibliographic record
Abstract
INTRODUCTION: With changes in norms related to cannabis use and in the regulation of cannabis, understanding trends in cannabis use is important, especially differentiating between trends that affect cohorts of all ages similarly, versus trends that disproportionately affect a younger generation. The present study examined the age-period-cohort (APC) effects on monthly cannabis use among adults in Ontario, Canada over a 24-year period. METHODS: Data were utilised from the Centre for Addiction and Mental Health Monitor Survey, an annual repeated cross-sectional survey of adults 18 years of age and older. The present analyses focused on the 1996 to 2019 surveys, which employed a regionally stratified sampling design using computer-assisted telephone interviews (N = 60,171). Monthly use of cannabis stratified by sex were examined. RESULTS: There was about a five-fold increase in monthly cannabis use from 1996 (3.1%) to 2019 (16.6%). The youngest adults use cannabis monthly more, but the patterns of monthly cannabis use appeared to be increasing among older adults. Adults born in the 1950s had higher prevalence of cannabis use (1.25 times more likely to use) compared to those born in 1964, with strongest period effect in 2019. The subgroup analysis of monthly cannabis use by sex showed little variation in APC effects. DISCUSSION AND CONCLUSIONS: There is a change in patterns of cannabis use among older adults and inclusion of birth cohort dimension improves the explanation of cannabis use trends. Adults in the 1950s birth cohort and increases in the normalisation of cannabis use could also be the key to explaining increasing monthly cannabis use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".