Non-medical prescription stimulant use to improve academic performance among Australian university students: prevalence and correlates of use
Bibliographic record
Abstract
BACKGROUND: Some university students consume pharmaceutical stimulants without a medical prescription with the goal of improving their academic performance. The prevalence of this practice has been well documented in the US, but less so in other countries. The potential harms of using prescription stimulants require a better understanding of the prevalence of this practice within Australian universities. METHODS: An internet survey of 1136 Australian students was conducted in 2015 in three large Australian universities. Students were asked about their personal use of prescription stimulants, attitudes and experiences with prescription stimulants. They were also asked about their use of caffeine, energy drinks and illicit drugs to enhance their academic performance. RESULTS: Lifetime self-reported use of stimulant medication to improve academic performance was 6.5, and 4.4% in the past year. Students were far more likely to report using coffee and energy drinks (41.4 and 23.6% respectively, lifetime use) than prescription stimulants to help them study and complete university assessments. Non-medical use of prescription stimulants was strongly associated with a history of illicit drug use. CONCLUSION: The prevalence of nonmedical prescription stimulant use to improve academic performance is low among university students in Australia, especially when compared with their use of coffee and energy drinks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".