Early‐onset smoking and vaping of cannabis: Prevalence, correlates and trends in New Zealand 14–15‐year‐olds
Bibliographic record
Abstract
INTRODUCTION: Initiating cannabis use at an early age elevates risk of harm. Cannabis vaping is an emerging issue, and it is unknown whether the patterning and correlates of early-onset cannabis vaping differ from those of cannabis smoking. METHODS: We used repeat cross-sectional data from a nationally representative biennial survey (2012-2018) of students aged 14-15 years in New Zealand (N = 11,405), response rate 65% (2012), 64% (2014-2016) and 59% (2018). RESULTS: Between 2012 and 2018 lifetime cannabis use decreased, but regular use (past month, weekly, daily) was stable. Prevalence of past month, weekly and daily use in 2016-2018 (pooled) was 8.6%, 3.4% and 1.5%, respectively. Cannabis vaping was reported by 24% of past month cannabis users. The demographic profile of early-onset cannabis smokers and vapers was similar, with elevated use of both modes among Māori (Indigenous), same- or both-sex attracted students and those in low decile (high-deprivation) schools. Correlates were similar for both modes. Cannabis use was strongly associated with tobacco and alcohol use. The next strongest associations (after adjustment) were exposure to second-hand smoke at home, student income >$50/week and low parental monitoring of whereabouts. Past week social media use, psychological distress and low parental monitoring of spending were also associated with both modes. DISCUSSION AND CONCLUSIONS: Early-onset cannabis use is much higher in structurally disadvantaged groups, and among those who use tobacco and alcohol. Comprehensive multisubstance approaches to prevention are indicated in this age group. Efforts to reduce socio-economic inequity and exposure to other risk factors may reduce cannabis-related harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".