Testing the robustness of daily associations of affect with alcohol and cannabis use
Bibliographic record
Abstract
Etiological models of alcohol and cannabis use disorders hypothesize that people are more likely to use substances when experiencing heightened negative affect, yet recent EMA studies found no evidence for this daily association. To provide a robust understanding of whether and when affect regulation is supported in EMA, we tested within-person associations between affect and substance use across hundreds of statistical models in a diverse sample of young adults (N = 496) recruited from both college and community sources, aged 18-22 years (55.8% assigned female sex at birth, 44.2% assigned male sex at birth; 47.2% cisgender female, 43.8% cisgender male, 12.9% nonbinary/genderqueer/gender non-conforming, 4.0% transgender; 69.6% non-Hispanic White, 26.2% Asian, 6.7% African American, 8.5% Hispanic/Latino). Using specification curve analyses, we examined how different affect operationalizations, time scales, and moderators influenced these associations. For alcohol use, higher negative affect predicted decreased likelihood of drinking (median OR = 0.95, p < .001), with 20.6% of specifications reaching significance. This counter-intuitive pattern was strongest for sadness and when examining maximum daily negative affect. Surprisingly, and contrary to theoretical predictions, this negative association was slightly more pronounced among those with higher coping motives and at lower levels of AUD symptoms. Positive affect showed a complex pattern, with high-arousal states like joviality strongly predicting increased drinking likelihood, while low-arousal states showed weaker associations. Neither affect type consistently predicted drinking quantity. For cannabis use, neither positive nor negative affect predicted use likelihood or quantity across specifications. These associations remained consistent regardless of substance use disorder severity or social context. Our findings challenge core assumptions of affect regulation models and suggest that, at least in young adults, the affect-substance use relationship is more nuanced than previously theorized, with implications for refining etiological models.
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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 | 0.029 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".