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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".