Investigating predictors of problematic alcohol, cannabis, and nicotine use among legal users of all three substances
Bibliographic record
Abstract
Background: The three most used substances-alcohol, cannabis, and nicotine-are frequently concurrently. Use of each substance has been connected to an increased probability of use of the other substances, and the problematic use of each substance has been linked to demographic factors, substance use factors, and personality. However, little is known about which risk factors are most important for consumers of all three substances. This study examined the extent to which various factors are associated with dependence on alcohol, cannabis, and/or nicotine in users of all three substances. Methods: 516 Canadian adults with past month use of alcohol, cannabis, and nicotine completed online surveys querying their demographics, personality, substance use history, and levels substance dependence. Hierarchical linear regressions were used to determine which factors best predicted levels of dependence on each substance. Results: Alcohol dependence was associated with levels of cannabis and nicotine dependence, and impulsivity, with 44.9% of variance explained. Cannabis dependence was predicted by alcohol and nicotine dependence levels, impulsivity, and the age of onset of cannabis use, with 47.6% of variance explained. Nicotine dependence was best predicted by alcohol and cannabis dependence levels, impulsivity, and dual use of cigarettes and e-cigarettes, with 19.9% of variance explained. Conclusions: Alcohol dependence, cannabis dependence, and impulsivity were the strongest predictors for dependence on each of the substances. A strong relationship between alcohol and cannabis dependence was evident, warranting further research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".