A qualitative study of the perceived effects of alcohol on depressive symptoms among undergraduates who drink to cope with depression
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Heavy drinking is prevalent among undergraduate students and is linked with drinking to cope with depression motives for drinking. Drinking to cope with depression remains poorly understood given that alcohol has been shown to have adverse effects on mood when consumed at high doses. Using semi-structured qualitative interviews, the present study examined the perceived effects of alcohol on depressive symptoms as reported by undergraduate students who endorse high levels of drinking to cope with depression. DESIGN AND METHODS: Sixteen undergraduate coping-with-depression-motivated (CWDM) drinkers (nine women, seven men), identified using the Modified Drinking Motives Questionnaire-Revised [1], reported on their experiences of drinking to cope with depression. Thematic analysis was conducted to identify themes and subthemes in the data. RESULTS: Undergraduate students reported several effects of alcohol on affective, cognitive and behavioural depressive symptoms. While most of the perceived alcohol effects they described involved relief from depressive symptoms, some perceived effects involved worsening depressive symptoms. DISCUSSION AND CONCLUSIONS: The study generated several hypotheses to explain drinking to cope with depression, some of which might be testable in future experimental work. Overall, findings suggest the mood-altering effects of alcohol do not fully explain why depression and alcohol use are frequently co-morbid. Indeed, effects of alcohol on cognitive and behavioural depressive symptoms might be particularly reinforcing for CWDM drinkers. Interventions that target co-morbid depression and alcohol use might be improved by teaching CWDM drinkers skills to reduce depressive cognitions and to improve interpersonal interactions outside of drinking contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".