Licit psychostimulant consumption in Australia, 1984–2000: international and jurisdictional comparison
Bibliographic record
Abstract
OBJECTIVES: To examine trends in the licit consumption of the psychostimulants dexamphetamine and methylphenidate in Australia and nine other countries from 1994 to 2000 and in each State and Territory of Australia from 1984 to 2000. DESIGN: Annual rates of consumption of psychostimulants were compared using Poisson regression models. All drug consumption was standardised to defined daily doses per 1000 population per day. MAIN OUTCOME MEASURES: Rates of consumption of each psychostimulant in each country and in each Australian State and Territory. RESULTS: For the 10 countries from 1994 to 2000, total psychostimulant consumption increased by an average 12% per year, with the highest increase from 1998 to 2000. Australia and New Zealand ranked third in total psychostimulant use after the United States and Canada. Australia consumed significantly more than the United Kingdom, Sweden, Spain, the Netherlands, France or Denmark. In Australia, from 1984 to 2000, the rate of consumption of licit psychostimulants increased by 26% per year, with an 8.46-fold increase from 1994 to 2000. Western Australia ranked first, with nearly twice the consumption rate of total psychostimulants as New South Wales, which ranked second. Methylphenidate is the main psychostimulant consumed in the US and Canada, and dexamphetamine in Australia. CONCLUSIONS: The consumption of psychostimulants in Australia is high internationally and varies significantly between States and Territories. The results imply varied jurisdictional prescribing determinants and supply processes throughout Australia, which may require new national prescribing standards and access to online patient data for prescribers and dispensers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".