Factors associated with permissive attitudes of university students towards prescription medication misuse
Bibliographic record
Abstract
Background University students with more permissive attitudes toward prescription medication misuse are more likely to engage in such misuse. This study investigated the factors associated with attitudes toward prescription medication misuse, and whether these factors differ between misuse for academic vs. recreational purposes.Methods An online survey included standardized measures of anxiety, depression, insomnia symptoms, daytime sleepiness, sleep quality, and attitudes toward prescription medication misuse. Participants were 3,160 university students aged 18–35 years. Univariate and multivariate linear regressions examined factors associated with attitudes toward prescription medication misuse.Results Factors associated with more permissive attitudes were nonwhite ethnicity, international student status, alcohol, tobacco, and nicotine vapor use, depressive symptoms, and clinical level of anxiety symptoms. Female gender, part-time study, and mild anxiety symptoms were associated with less permissive attitudes toward prescription medication misuse. Older age was associated with more permissive attitudes toward medication misuse for recreational purposes. No factors were associated with attitudes toward misuse for academic purposes.Conclusions Groups that have more permissive attitudes toward prescription medication misuse could be targeted by prevention programs. More investigation is needed regarding attitudes toward academic vs. recreational misuse of prescription medication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".