Cannabis and other drug use by tertiary students in Darwin, Northern Territory, Australia
Bibliographic record
Abstract
Abstract Cannabis has been reported to be associated with impaired educational attainment in adolescents, reduced school performance and the potential for underperformance in adults engaged in occupations requiring high-level cognitive skills. The current study examined the extent and patterns of cannabis and other drug use among 386 tertiary students in Darwin, Northern Territory, Australia. The sample was mainly female (72%), half were aged under 25 years and 52% were in the first year of tertiary study. Cannabis use was prevalent among students, with 68.3% ever using it, 32.4% in the last year and 22.4% with recent use (last six months). The current pattern of cannabis use was significantly associated with age and gender. The most common reasons for using cannabis were to unwind (45.6%) or become stoned (33.7%). Close to half (52.3%) of recent users were not at all concerned about their cannabis use and 63.2% did not think they needed to reduce consumption. In the last six months, 84.5% of students had used alcohol, 12.1% amphetamine, 8.4% ecstasy, 6.8% non-medical use of benzodiazepines, 4.6% hallucinogens, 1.6% inhalants and 1.1% opiates. A quarter (23.6%) of students had used alcohol and cannabis on the same occasion. The results are discussed in relation to the utility of traditional awareness programs and the desirability of appropriate and credible intervention strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".