‘Sorry, I don’t give away my medication’: an examination of refusals to divert stimulant medication
Bibliographic record
Abstract
Background: Prescription stimulant misuse is a common illicit behavior on college campuses and is fueled, in part, by prescription stimulant diversion (PSD), which involves giving away, selling, or trading one's medication. Being approached for one's medication predicts PSD; however, no research has examined resistance strategies used by prescribed students in the real world. Method: =20.25). We used deductive content analysis to categorize resistance strategies and examined group differences (treatment vs. placebo) in strategy use, and whether using strategies perceived as more effective in prior research (explanations, direct refusals, alternatives) was associated with less PSD or being approached. Results: We identified four strategies consistent with prior literature: explanations (49%), excuses (22%), direct refusals (17%), and alternatives (6%) and one novel but uncommonly used strategy: non-response (i.e., deflecting/ignoring) (6%). Chi-square tests showed that treatment and placebo did not differ in use of strategies perceived to be more effective; however, students who consistently (vs. inconsistently) used these strategies had a lower risk of any PSD at 6 months. Most students (70-71%) reporting PSD also refused a medication request and a t-test showed these students were approached more often. Conclusions: Students largely use strategies perceived as effective for resisting stimulant requests; nonetheless, most students who divert also resist requests, suggesting these behaviors are not mutually exclusive. Interventions that encourage consistent use of explanations, direct refusals, and alternatives may curb PSD. Registered on ClinicalTrials.gov on 05/12/2021: NCT04885166.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".