OR14-1 * PATTERNS AND TRANSITIONS IN SUBSTANCE USE AMONG YOUNG SWISS MEN
Bibliographic record
Abstract
Introduction. The stages of involvement in illicit drugs other than cannabis remain vague and few studies focused on the last steps of drug-use trajectories. This study investigated this topic. Methods. We used data from the Swiss Longitudinal Cohort Study on Substance Use Risk Factors (C-SURF) to assess exposure to drug use (alcohol, tobacco, 16 illicit drugs including heroin, and five prescription drugs including opioids) at two times point (N = 5,041). Patterns and trajectories of drug use were studied using latent transition analysis (LTA) and cross-lagged panel models. Results. The LTA identified five classes of drug users showing a pattern involving adding alcohol, tobacco, cannabis, middle-stage drugs (uppers, hallucinogens, inhaled drugs), and final-stage drugs (e.g. heroin, ketamine, crystal meth). The most common transition was to remain in the same latent class. Heroin use predicted later opioid use (b = .071, p = .003) but not the reverse (b = -.005, p = .950). Conclusion. The pattern of drug use displayed the well-known sequence of drug involvement (licit drugs/cannabis/other illicit drugs), but added a distinction between "middle-stage" and "final-stage" drugs. Progression along the whole drug course remained rare among participants in their twenties. For the final stage, heroin appeared as to be a step for opioid use.
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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".