Mitigating risks of students use of study drugs through understanding motivations for use and applying harm reduction theory: a literature review
Bibliographic record
Abstract
As postsecondary students' use of "study drugs" becomes more popular with increasingly reported negative effects on health and academic performance, failing prohibitionist policies to reduce consumption, and ambiguity in literature towards best practices to address this population, we present a literature review that seeks effective solutions educational institutions can apply to improve outcomes for students who use drugs. Motivations for use, effects of the substances, an analysis of efforts to control use from educational institutions, and suggestions on promoting most effective outcomes based on harm reduction, are described. Theory, quantitative, and qualitative works from systematic reviews, cohort studies, and epidemiological assessments are examined on the "study drugs" methylphenidate, dextroamphetamine, and amphetamine, also known as Adderall, Ritalin, Focalin, and Concerta. There is a focus on postsecondary students ages 18-25 in North America. Results show important risk factors for drug use including low perceived self-efficacy or enjoyment in courses, poor accommodation of special needs, reliance on external validation, having a low GPA, and experiencing a mental health issue. There is much misconception on the health and academic effects of these drugs in literature, among students, and on online knowledge sources. We suggest these drugs do not improve GPA and learning, while they might temporarily increase memory, but with detrimental negative health effects. Campaigns that address underlying factors of use can be most successful in mitigating harms.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".