Cannabis Use Among Remote Indigenous Australians: Opportunities to Support Change Identified in Two Waves of Sampling
Bibliographic record
Abstract
Background: Harms from cannabis use among Indigenous populations in Australia, New Zealand, Canada and the United States may be magnified by poorer health and heavy use. However, little direct evidence is available to evaluate cannabis’ impacts. In communities in remote northern Queensland (Australia) where cannabis has become endemic, opportunities to support change were investigated. Methods: Participants (aged 15-49 years) were asked about their cannabis use history in interviews in two waves of population sampling in Cape York (Queensland). Wave 1 included 429 people (235 males and 194 females); and wave 2 included 402 people (228 males and 174 females). Current users (used cannabis during the year before interview) described frequency of use, amount consumed, expenditure and dependence symptoms. Other substance use was recorded for 402 people at wave 2. Results: Wave 1: 69% reported lifetime use and 44% current use. Males (55%) were more likely than females (30%) to be current users (P0.001). Conclusions: High rates of heavy cannabis use in remote Australian Indigenous communities warrant further action. Successful cessation in a number of individuals suggests that significant opportunities are available
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".