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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".