Acute Stress Increases Voluntary Consumption of Alcohol in Undergraduates
Bibliographic record
Abstract
AIMS: The primary aim of this study was to assess whether an acute stressor directly increases alcohol intake among undergraduates. A secondary aim was to examine whether individual differences in state anxiety predict alcohol intake. METHOD: Following random assignment, undergraduate students (n = 75; 47% males; mean age = 20.1 ± 2.8) completed the Trier Social Stress Test or no-stress protocol, and then engaged in a 30-min free-drinking session (alcohol, placebo, or non-alcoholic beverage). The State-Trait Anxiety Inventory was completed upon arrival, post-stressor, and after drinking. RESULTS: Planned comparisons demonstrated that psychosocial stress increased voluntary intake of alcohol, but not placebo or non-alcoholic beverages. In linear regression analyses, individual differences in anxiety did not predict voluntary alcohol consumption. CONCLUSION: A proximal relationship exists between acute stress and single-session alcohol intake in undergraduates, which may explain the relationship between life stressors and increased drinking in this group. These findings demonstrate that stress management is an important target for reducing heavy episodic drinking on university campuses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".