Substance Use Motives as Mediators of the Associations between Self-Control Constructs and Negative Substance Use Consequences: A Cross-Cultural Examination
Bibliographic record
Abstract
The present study sought to examine three distinct research questions: a) are self-control constructs (i.e., negative/positive urgency, self-regulation, and emotion-regulation) indirectly related to negative alcohol/marijuana consequences via substance use motives, b) to what extent are these indirect effects consistent across differing drugs (i.e., alcohol and marijuana), and c) are these models invariant across gender and countries. Participants were 2,230 college students (mean age=20.28, SD=0.40; 71.1% females) across 7 countries (USA, Canada, Spain, England, Argentina, Uruguay, and South Africa) who consumed alcohol and marijuana in the last month. Two (one for alcohol and one for marijuana) fully saturated path models were conducted, such that indirect paths were examined for each self-control construct and substance use motive on negative consequences (e.g., negative urgency → coping motives → negative consequences) within the same model. Within the comprehensive alcohol model, we found that lower self-regulation and higher negative urgency/suppression were related to more alcohol consequences via higher coping and conformity motives. For marijuana, we found that lower self-regulation and higher negative urgency/suppression were related to more marijuana consequences via higher coping motives (not significant for conformity motives). Unique to marijuana, we did find support for higher expansion motives indirectly linking positive urgency to more negative consequences. These results were invariant across gender groups and only minor differences across countries emerged. Prevention and intervention programs of alcohol and marijuana around university campuses may benefit from targeting self-control related skills in addition to motives to drug use to prevent and reduce negative drug-related consequences.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".