Enhancement motives moderate the relationship between high‐arousal positive moods and drinking quantity: Evidence from a 22‐day experience sampling study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Individuals who consume alcohol may be distinguished by their drinking motives. Enhancement motives involve drinking to enhance positive moods. Research on the moderating effect of enhancement motives on the within-person relation between daily positive mood and drinking has not differentiated between high- (e.g. hyper) and low-arousal (e.g. cheerful) positive moods. The present study addressed this limitation. We hypothesised that enhancement motives would positively moderate the relationship between mid-afternoon high-arousal positive mood and evening drinking. DESIGN AND METHODS: Using a palm pilot-based experience sampling design, 143 undergraduate drinkers answered daily surveys assessing positive mood (mid-afternoon) and drinks (evening) for 22 consecutive days. RESULTS: As hypothesised, enhancement motives strengthened the relation between high-arousal positive moods and drinking. Upon closer examination, the mood-drinking slope for those high in enhancement motives was unexpectedly flat, whereas the mood-drinking slope for those low in enhancement motives was negative. DISCUSSION AND CONCLUSIONS: We demonstrated that high enhancement-motivated drinkers exhibit a high, stable drinking level, regardless of the intensity of their high-arousal positive mood. In contrast, low enhancement-motivated drinkers decrease their drinking when in a high-arousal positive mood state. Clinicians may be able to help reduce heavy alcohol consumption in enhancement-motivated drinkers by teaching them to reduce their drinking when in a high-arousal positive mood state.
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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 | 0.002 | 0.006 |
| 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) | 0.003 | 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".