Depressive Symptoms and Alcohol Problems Among Emerging Adults: Cross-Sectional and Daily Diary Findings
Bibliographic record
Abstract
Depressive symptoms and alcohol problems frequently co-occur, particularly among emerging adults (EAs; ages 18-29), who exhibit the highest rates of mood disorders and alcohol problems across age groups. Rumination, closely linked with depression, has also been implicated in problem drinking. However, research examining the role of rumination in the depressive symptoms–alcohol problems relationship in EAs remains limited. The aim of this project was to empirically examine the interplay between these variables, to ultimately inform clinical practice.In Study 1, I utilized a cross-sectional design to preliminarily test the indirect effect of rumination on the relationship between depressive symptoms–alcohol problems relationship and the moderating role of gender. EAs (n = 314; Mage = 24.95; SD = 2.77) completed measures assessing depressive symptoms, rumination, and past 30-day alcohol use and problems. Results supported an indirect effect of depressive symptoms on alcohol problems through rumination, though gender did not moderate this pathway. However, gender moderated the direct relationship, with depressive symptoms significantly associated with alcohol problems only among women. In Study 2, I utilized daily diary data collection to gain a more nuanced understanding of these relationships. Participants (n = 46, Mage = 24.49; SD = 2.95) completed a baseline survey and daily measures of rumination and drinking behaviour. Within individuals, daily rumination was not significantly associated with daily alcohol problems. However, between individuals, rumination was positively associated with alcohol problems during the diary period, and this association was moderated by baseline depressive symptoms. The relationship between rumination and alcohol problems strengthened with higher depressive symptoms. These findings highlight rumination as a key risk factor for alcohol problems in EAs, particularly those with elevated depressive symptoms. The results underscore the importance of using varied methodologies to examine psychological constructs. The significant between-subjects association suggests that trait rumination, rather than state rumination, may be a stronger predictor of alcohol problems. Clinically, assessing and addressing rumination should be prioritized when intervening or preventing high-risk drinking in EAs, especially those experiencing depressive symptoms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".