Coping-anxiety and coping-depression motives predict different daily mood-drinking relationships.
Bibliographic record
Abstract
Individuals with different drinking motives show distinctive patterns of alcohol use and problems. Drinking to cope, or endorsing strong coping motives for alcohol use, has been shown to be particularly hazardous. It is important to determine the unique triggers associated with coping drinking. One limitation of past research has been the failure to contend with the complexities inherent in coping motives. Using the Modified Drinking Motives Questionnaire-Revised (Grant, Stewart, O'Connor, Blackwell, & Conrod, 2007), which separates coping-anxiety and coping-depression motives, we investigated whether these motives moderated relationships between daily mood and subsequent drinking (statistically controlling for sex, baseline anxious and depressive symptomatology, initial alcohol problems, and additional drinking motives). College students (N=146) provided daily reports of mood and alcohol consumption online for 3 weeks. Multilevel modeling analyses revealed that, as hypothesized, stronger initial coping-depression motives predicted higher daily depressed mood-alcohol consumption slopes. Also consistent with expectation, stronger initial coping-anxiety motives predicted higher anxious mood-alcohol consumption slopes. We discuss how this identification of the unique mood triggers associated with each type of coping drinking motive can provide the basis for targeted interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".