The Swiss Cohort Study on Substance Use Risk Factors – Findings of two Waves
Bibliographic record
Abstract
Abstract. Aim: To summarize published findings in peer-reviewed journals of the first two waves of the Swiss Cohort Study on Substance Use Risk Factors (C-SURF), a longitudinal study assessing risk and protective factors of 5,987 young men during the phase of emerging adulthood (20 years at baseline, followed-up 15 months later). Methods: Included were 33 studies published until November 2014 focusing on substance use. Results: Substance use in early adulthood is a prevalent and stable behavior. The 12-month prevalence of nonmedical use of prescription drugs (10.6 %) lies between that of cannabis (36.4 %) and other illicit drugs such as ecstasy (3.7 %) and cocaine (3.2 %). Although peer pressure in the form of misconduct is associated with increased substance use, other aspects such as peer involvement in social activities may have beneficial effects. Regular sport activities are associated with reduced substance use, with the exception of alcohol use. Young men are susceptible to structural conditions such as the price of alcohol beverages or the density of on-premise alcohol outlets. Particularly alcohol use in public settings such as bars, discos or in parks (compared with private settings such as the home) is associated with alcohol-related harm, including injuries or violence. Being a single parent versus nuclear family has no effect on alcohol use, but active parenting does. Besides parenting, religiousness is an important protective factor for both legal and illegal substance use. Merely informing young men about the risks of substance use may not be an effective preventive measure. At-risk users of licit and illicit substances are more health literate, e. g., for example, they seek out more information on the internet than non-at-risk-users or abstainers. Discussion: There are a number of risk and protective substance use factors, but their associations with substance use do not necessarily agree with those found outside Europe. In the United States, for example, heavy alcohol use in this age group commonly takes place in private settings, whereas in Switzerland it more often takes place in public settings. Other behaviors, such as the nonmedical use of prescription drugs, appear to be similar to those found overseas, which may show the need for targeted preventive actions. C-SURF findings point to the necessity of establishing European studies to identify factors for designing specific preventive actions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".