Distress Tolerance Predicts Substance Use Motivations and Problems in Young Adults Across Four Continents
Bibliographic record
Abstract
Introduction: People low in trait distress tolerance are at higher risk for harmful patterns of substance use. Some evidence suggests that maladaptive motives for substance use account for this correlation. However, the generality of these associations remains in doubt because virtually all available data come from North American samples.Method: Using data from 7 countries (total N = 5,858; US, Argentina, Uruguay, Spain, South Africa, Canada, and England), we examined distress tolerance’s association with alcohol- and cannabis-related problems in young adults. We then investigated whether motivational dimensions for substance use (i.e., coping, conformity, social, enhancement, expansion) mediated this association.Results: We found that distress tolerance was inversely related to problematic alcohol and cannabis use (rs = -.14 and -.13). Mediation analyses were consistent with the possibility that people with low distress tolerance experience more substance-related problems because they are driven to use alcohol and cannabis to neutralize negative emotions and avoid social rejection. Zero-order and indirect effects did not vary substantially across regions of the world.Conclusions: Distress tolerance’s role in substance-use problems probably generalizes beyond North America, and distress tolerance’s connection with negative reinforcement processes (e.g., coping and conforming motives) may create vulnerability to problematic substance 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".