Gender Differences in Mindfulness, Self-Compassion, and Drinking to Cope in Undergraduates with Problematic Consumption
Bibliographic record
Abstract
Undergraduate students show the highest rates of problematic alcohol consumption compared to any other non-clinical category of individuals, and coping-motivated drinking has been consistently shown to be the most problematic. The present study examines associations between mindfulness facets, self-compassion, and coping-motivated drinking, and how these associations differ by gender. Undergraduate problematic drinkers (N = 146) completed self-report measures assessing their motives for drinking (coping-depression, coping-anxiety, enhancement, social, conformity) and levels of dispositional mindfulness (observing, describing, acting with awareness, non-judging, non-reactivity) and self-compassion. Regression analyses revealed that for both genders, mindfulness facets and self-compassion were statistically significantly negatively associated with coping-depression, but not coping-anxiety. Non-judging was uniquely associated with coping-depression in women, but in men, non-reactivity was the sole unique association. Unexpectedly, describing was negatively associated with conformity-motivated drinking in women. Mindfulness and self-compassion based programs for undergraduate problematic drinkers may be most effective if they target students who drink to cope with depression and emphasize different skills depending on the student’s gender.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".