Stigma Related to the Non-Medical Use and Diversion of Prescription Stimulant Drugs: Should We Care
Bibliographic record
Abstract
BACKGROUND: Non-medical use (NMU) and diversion of prescription stimulants are prevalent on college campuses. Diversion represents a primary source of acquisition for NMU among young adults. This study examined relationships between stigmatizing beliefs related to NMU and diversion of stimulant medications and engagement in these behaviors, as well as how such perceptions are associated with indicators of psychological distress among those who engage in these behaviors. METHODS: = 384) were recruited from a large US university to participate in this cross-sectional electronic survey-based study. Relationships between stigma variables and NMU and diversion were assessed. Among those who engage in NMU and diversion, we tested relationships between stigma variables and indicators of psychological distress, using validated instruments. RESULTS: Perceived social and personal stigmatic beliefs did not significantly predict NMU. However, perceived social and personal stigma of diversion significantly reduced diversion likelihood. For NMU, associations were found between stigma variables and indicators of psychological distress. Markedly, we found that as stigmatic perceptions of NMU increased, so did depressive, anxiolytic, and suicidal symptomatology among those who engage in NMU. CONCLUSIONS: Stigmatization does not deter NMU; however, stigmatization is positively associated with psychological harm among those who engage in NMU. Interventions should be developed to reduce stigmatization in order to improve psychological health among those who engage in NMU. Stigmatic perceptions of diversion were not predictive of psychological harm, though they are negatively associated with diversion behavior.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".