Monitoring the stability of risk factors for adolescent cannabis use
Bibliographic record
Abstract
Hallgren, M., & Kallmén, H. (2014). Monitoring the stability of risk factors for adolescent cannabis use. The International Journal Of Alcohol And Drug Research, 3(4), 235-243. doi:http://dx.doi.org/10.7895/ijadr.v3i4.184Aims: To identify factors associated with repeated cannabis use among Swedish adolescents aged 15 and 17 years and assess the stability of these factors over time, in the context of rising cannabis use and recent socio-economic changes. Design: Two cross-sectional surveys completed in 2006 and 2012 are compared.Setting: Secondary schools in Stockholm, Sweden.Participants: 15- and 17-year-old secondary-school students surveyed in 2006 (n = 11,895) and 2012 (n = 13,004). Response rates were 76% and 77%, respectively.Measures: Bivariate and multivariate analyses of the Stockholm Student Survey identified associations between repeated cannabis use (2 to 4 times or more) and 20 presumed risk factors.Findings: Despite socio-economic changes in Sweden and recent increases in cannabis use, the factors associated with repeated cannabis use among adolescents have remained stable in recent years. Four key variables were identified in both survey years: having drug-using friends, cigarette smoking, early alcohol debut, and high drug availability.Conclusions: Multi-component prevention strategies that ameliorate peer influences on drug taking and reduce cigarette smoking are highly recommended. Preventing the initiation of alcohol consumption at an early age and reducing drug availability may also reduce the risk of cannabis use.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".