A Latent Class Analysis of Polysubstance Use Patterns and Their Association with Ruminative Thinking Styles, Impulsivity-Like Traits, and Adverse Childhood Experiences Among College Students from Seven Countries
Bibliographic record
Abstract
Among college students, research has found distinct classes of polysubstance use patterns differentially associated with negative consequences. However, there is less clarity regarding how vulnerability factors discriminate across polysubstance use types and cross-cultural variability in these patterns. In addressing these gaps, we identified typologies of substance use based on reported lifetime use of a set of substances in college students from seven countries. We also examined mean differences across classes on ruminative thinking styles, impulsivity-like traits, and adverse childhood experiences; and compared the proportion of students in each subgroup between pairs of countries. College students located in the U.S., Canada, South Africa, Spain, Argentina, England, and Uruguay completed an online survey following a convenience sampling procedure (n = 9,065; 71% women). Using latent class analysis, we identified Class 1 “Polysubstance Users”, Class 2 “Alcohol, Marijuana, and Tobacco Co-Users”, and Class 3 “Drinkers”. Class 1 exhibited greater adverse childhood experiences, higher ruminative thinking, and greater impulsivity than Classes 2 and 3. Our results suggest that the U.S. was more similar to Spain, Argentina, and Uruguay in alcohol, marijuana, and tobacco co-use than in the other two classes. Additionally, the U.S. was more similar to South Africa regarding polysubstance use than the other classes. Most participants exhibited polysubstance use and constructs with the potential of being targeted in interventions discriminating against these classes. Findings highlight the pervasiveness of these patterns, indicating a need for global prevention efforts to reduce the likelihood of engaging in polysubstance 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".