Adolescent alcohol drinking and cannabis use: Longitudinal, and cohort analysis of high school students in Australia
Bibliographic record
Abstract
Aims: The final year of high school is a challenging phase, during which substance use is common. We conducted longitudinal and cohort comparisons on the levels of alcohol and cannabis use among final year (Year 12) high school students compared to the previous year. Design: Longitudinal and cohort analyses of self-reported survey data. Setting: Ten independent schools across South-East Queensland, Australia. Participants: Year 12 students in 2020 (n = 1024) were compared (a) longitudinally with themselves in Year 11; and (b) to the 2019 Year 12 cohort (n = 632). Measures: Self-reported alcohol and cannabis use. Analyses adjusted for socio-demographic, parental, and schooling variables. Findings: Longitudinally, Year 12 students of 2020 had higher odds of having six or more drinks per occasion, monthly or more often, and reporting lifetime cannabis use, compared to themselves in 2019. However, they were not more likely to drink alcohol weekly or more often in 2020 versus 2019. Compared to the 2019 cohort, the 2020 cohort had higher odds of drinking weekly or more often, having six or more drinks per occasion monthly, and reporting lifetime cannabis use. Conclusions: The 2020 cohort of Year 12 adolescents were more likely to engage in heavy drinking and cannabis use, compared to themselves the previous year, and compared to the previous cohort. Greater alcohol consumption and likelihood of cannabis use among the 2020 cohort might be explained by increased age and impacts of the COVID-19 pandemic. Future research to monitor if this is a continuing trend is warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".