Binge drinking indirectly predicts a negative emotional memory bias through coping motivations and depressive symptoms: The role of sex/gender
Bibliographic record
Abstract
Background: In this three-part study, we investigate whether the associations between binge and problematic drinking patterns with a negative emotional memory bias (NMB) are indirectly related through coping motivations and depressive symptoms. We also address potential sex differences in these relations. Methods: = 293) completed the Timeline Followback to assess binge drinking, the Alcohol Use Disorder Identification Test (AUDIT) to assess problematic alcohol use, the Drinking Motives Questionnaire-Revised to assess coping motivations, and the Depression, Anxiety, and Stress Scales-21 to assess depression. Participants were asked to identify whether 30 emotional sentences were self-referent or not in an incidental encoding task; 24 h later they were asked to recall as many sentences as possible and a negative memory bias score was calculated. Results: Across all three studies, we found significant bivariate relations between AUDIT scores, coping, depression, and an NMB, particularly for sentences participants deemed self-referent. In two undergraduate samples, there were significant indirect effects through coping motivations and depressive symptoms between binge drinking and an NMB in females as well as between AUDIT scores and an NMB in females only. In the community sample, there was only an indirect effect through coping motives, but this was observed in both females and males. Conclusion: These findings support a relation between binge drinking as well as problematic alcohol use and a self-referent NMB in the context of coping motivations for alcohol use and depressive symptoms. Moreover, the pattern of findings suggests this model primarily holds for females, yet may also apply to males at higher levels of problematic alcohol use.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".