Association between non‐medical prescription drug use and personality traits among young <scp>S</scp>wiss men
Bibliographic record
Abstract
AIM: To investigate the relationships between six classes of non-medical prescription drug use (NMPDU) and five personality traits. METHODS: Representative baseline data on 5777 Swiss men around 20 years old were taken from the Cohort Study on Substance Use Risk Factors. NMPDU of opioid analgesics, sedatives/sleeping pills, anxiolytics, antidepressants, beta-blockers and stimulants over the previous 12 months was measured. Personality was assessed using the Brief Sensation Seeking Scale; attention deficit-hyperactivity (ADH) using the Adult Attention-Deficit-Hyperactivity Disorder Self-Report Scale; and aggression/hostility, anxiety/neuroticism and sociability using the Zuckerman-Kuhlmann Personality Questionnaire. Logistic regression models for each personality trait were fitted, as were seven multiple logistic regression models predicting each NMPDU adjusting for all personality traits and covariates. RESULTS: Around 10.7% of participants reported NMPDU in the last 12 months, with opioid analgesics most prevalent (6.7%), then sedatives/sleeping pills (3.0%), anxiolytics (2.7%), and stimulants (1.9%). Sensation seeking (SS), ADH, aggression/hostility, and anxiety/neuroticism (but not sociability) were significantly positively associated with at least one drug class (OR varied between 1.24, 95%CI: 1.04-1.48 and 1.86, 95%CI: 1.47-2.35). Aggression/hostility, anxiety/neuroticism and ADH were significantly and positively related to almost all NMPDU. Sociability was inversely related to NMPDU of sedatives/sleeping pills and anxiolytics (OR, 0.70; 95%CI: 0.51-0.96 and OR, 0.64; 95%CI: 0.46-0.90, respectively). SS was related only to stimulant use (OR, 1.74; 95%CI: 1.14-2.65). CONCLUSION: People with higher scores for ADH, aggression/hostility and anxiety/neuroticism are at higher risk of NMPDU. Sociability appeared to protect from NMPDU of sedatives/sleeping pills and anxiolytics.
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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 | 0.002 | 0.002 |
| 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".