Routes of Administration of Illicit Drugs among Young Swiss Men: Their Prevalence and Associated Socio-Demographic Characteristics and Adverse Outcomes
Bibliographic record
Abstract
The prevalence of different routes of administration (ROAs) of illicit drugs other than cannabis was examined in young Swiss men, in addition to the association between socio-demographics and adverse outcomes and particular ROAs. Our sample consisted of 754 men (mean age = 25.4 ± 1.2 years) who participated in the Cohort Study on Substance Use Risk Factors and reported using any of 18 illicit drugs over the last 12 months. Prevalence estimates were calculated for oral use, nasal use, smoking, injecting, and other ROAs. Associations between ROAs and socio-demographics and adverse outcomes (i.e., alcohol use disorder (AUD), suicidal ideations, and health and social consequences) were calculated for using single versus multiple ROAs. The most prevalent ROA was oral use (71.8%), followed by nasal use (59.2%), smoking (22.1%), injecting (1.1%), and other ROAs (1.7%). Subjects' education, financial autonomy, and civil status were associated with specific ROAs. Smoking was associated with suicidal ideations and adverse health consequences and multiple ROAs with AUD, suicidal ideations, and health and social consequences. The most problematic pattern of drug use among young adults appears to be using multiple ROAs, followed by smoking. Strategies to prevent and reduce the use of such practices are needed to avoid adverse outcomes at this young age.
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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 | 0.001 |
| 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".