Sensation seeking, drinking motives, and going out mediate the link between eveningness and alcohol use and problems in adolescence
Bibliographic record
Abstract
The present study was aimed at obtaining a more comprehensive understanding of the possible mediating factors in the morningness-eveningness → alcohol consumption relation. We explored the role of mediators such as sensation seeking, the frequency of going out in the evenings, and drinking motives. We tested the proposed mediation model via structural equation modeling based on cross-sectional research conducted among Hungarian adolescents and young adults (N = 1695, 42.5% male, Mage = 18.98, SDage = 1.89). The outcomes included the frequency of alcohol consumption and problematic drinking. Both alcohol consumption and problematic drinking were predicted in similar directions and magnitudes by sensation seeking, “going out,” and coping drinking motives. However, eveningness still had a significant direct effect on alcohol consumption and problematic drinking after we controlled for sensation seeking, going out, and drinking motives. For problematic drinking, the possible role of drinking motives seems to be higher and more complex than it is for alcohol consumption. The mediators, such as sensation seeking, the amount of time spent out in the evenings, and drinking motives, can explain the eveningness → alcohol consumption relationship and should be targeted for alcohol prevention programs among evening-type adolescents.
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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 | 0.001 |
| Research integrity | 0.000 | 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".