Does ruminating about the previous night’s drinking during a hangover predict changes in heavy episodic drinking? A two-wave, 30-day prospective study.
Bibliographic record
Abstract
OBJECTIVE: We examined whether hangover-related rumination-repeatedly dwelling on negative aspects of yesterday's drinking while hungover the following morning-predicts changes in three dimensions of heavy episodic drinking (HED) over time. METHOD: = 334 emerging adults (aged 19-29) from three Eastern Canadian universities who had recently experienced a hangover completed online self-report questionnaires at baseline (Wave 1) and 30 days later (Wave 2; 71.6% retention). HED was assessed in frequency (number of HED episodes), perceptions (how participants perceived the extent of their heavy drinking), and quantity (greatest number of alcoholic drinks consumed in a single HED episode) over the past 2 weeks. RESULTS: Levels of HED frequency, perceptions, and quantity declined overall from Waves 1 to 2. Exploratory factor analysis established two factors of hangover-related rumination: intrusiveness (unwanted thoughts about the previous night's drinking) and regret (desire to change future drinking behavior). Structural equation models revealed that intrusiveness at Wave 1 predicted the maintenance of higher frequency and perceptions of HED at Wave 2, even as these HED measures were generally declining; regret at Wave 1 also predicted the maintenance of HED perceptions at Wave 2. Neither Wave 1 hangover rumination factor predicted changes in HED quantity at Wave 2. Models controlled Wave 1 variables, including the relevant HED outcome, overall hangover severity, total number of hangovers, generalized anxiety symptoms, sex, age, and data collection site. CONCLUSION: Hangover-related rumination factors are associated with the maintenance of higher HED frequency (intrusiveness factor) and HED perceptions (intrusiveness and regret factors), suggesting risk for problematic alcohol consumption. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".