Pursued for their prescription: Exposure to compliance-gaining strategies predicts stimulant diversion in emerging adults.
Bibliographic record
Abstract
Researchers have begun to identify predictors of who will divert their stimulant prescriptions, as most emerging adults (EAs) who use prescription stimulants nonmedically procure these drugs from a friend or acquaintance with a prescription. Far less research has examined how EAs with attention-deficit/hyperactivity disorder (ADHD) are approached for these medications, and their affective and behavioral responses to these requests. We hypothesized that EAs with a stimulant prescription who reported greater exposure to compliance-gaining attempts from peers, particularly rational appeals for academic work, would be more likely to divert, as would EAs who reported lower resistance to peer influence. We recruited EAs diagnosed with ADHD (N = 149) through flyers, in-class presentations, campus-wide e-mails, and psychology subject pools at 2 demographically dissimilar college campuses. As predicted, a logistic regression showed that greater exposure to compliance-gaining strategies, Greek involvement, Northeast college attendance, and less guilt and worry about diversion predicted diversion (n = 53, 36%). Diverters were no less resistant to peer influence; however, a continuous measure assessing willingness to divert was inversely correlated with resistance to peer influence. An analysis of variance showed that rational appeals for academic work and guilt-inducing strategies for not complying with diversion requests were associated with the greatest likelihood of diversion. Further, negative affective responses (e.g., feeling manipulated, used) among students with a prescription following diversion were relatively common. Interventions to reduce diversion should inoculate EAs with ADHD against a range of compliance-gaining strategies and should help EAs who are experiencing dissonance about diversion to resist their peers' requests more effectively. (PsycINFO Database Record
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".