Dyadic conflict, drinking to cope, and alcohol-related problems: A psychometric study and longitudinal actor–partner interdependence model.
Bibliographic record
Abstract
The motivational model of alcohol use posits that individuals may consume alcohol to cope with negative affect. Conflict with others is a strong predictor of coping motives, which in turn predict alcohol-related problems. Two studies examined links between conflict, coping motives, and alcohol-related problems in emerging adult romantic dyads. It was hypothesized that the association between conflict and alcohol-related problems would be mediated by coping-depression and coping-anxiety motives. It was also hypothesized that this would be true for actor (i.e., how individual factors influence individual behaviors) and partner effects (i.e., how partner factors influence individual behaviors) and at the between- (i.e., does not vary over the study period) and within-subjects (i.e., varies over the study period) levels. Both studies examined participants currently in a romantic relationship who consumed ≥12 alcoholic drinks in the past year. Study 1 was cross-sectional using university students (N = 130 students; 86.9% female; M = 21.02 years old, SD = 3.43). Study 2 used a 4-wave, 4-week longitudinal design with romantic dyads (N = 100 dyads; 89% heterosexual; M = 22.13 years old, SD = 5.67). In Study 2, coping-depression motives emerged as the strongest mediator of the conflict-alcohol-related problems association, and findings held for actor effects but not partner effects. Supplemental analyses revealed that this mediational pathway only held among women. Within any given week, alcohol-related problems changed systematically in the same direction between romantic partners. Interventions may wish to target coping-depression drinking motives within couples in response to conflict to reduce alcohol-related problems.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".