Prevalence and factors associated with non-medical prescription stimulant use to promote wakefulness in young adults
Bibliographic record
Abstract
Objective: This study examined the prevalence and factors associated with non-medical use of prescription stimulants to promote wakefulness. Participants: We surveyed 3,160 university students aged 18–35 between June 2016 and May 2017. Method: Participants reported whether they used prescription stimulants non-medically to stay awake and completed measures of anxiety and depressive symptoms, sleep quality, insomnia, daytime sleepiness, and attitudes toward non-medical prescription drug use. Univariate and multivariate regression models were used. Results: Prevalence of non-medical prescription stimulant use to promote wakefulness was 3.1%. The following factors remained significant in the multivariate model: alcohol, tobacco, and nicotine vapor use, attitude toward non-medical use of prescription medication, poor sleep quality, and daytime sleepiness. Conclusion: Poor sleep, substance use and more liberal attitudes to non-medical prescription drug use were associated with the misuse of stimulants to promote wakefulness. Prevention/intervention programs should promote sleep hygiene and highlight the risks of using prescription drugs non-medically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".