Linking Masculinity to Negative Drinking Consequences: The Mediating Roles of Heavy Episodic Drinking and Alcohol Expectancies
Bibliographic record
Abstract
OBJECTIVE: This study extends previous research on masculinity and negative drinking consequences among young men by considering mediating effects of heavy episodic drinking (HED) and alcohol expectancies. We hypothesized that masculinity would have a direct relationship with negative consequences from drinking as well as indirect relationships mediated by HED and alcohol expectancies of courage, risk, and aggression. METHOD: A random sample of 1,436 college and university men ages 19-25 years completed an online survey, including conformity to masculine norms, alcohol-related expectancies, HED, and negative drinking consequences. Regression analyses and structural equation modeling were used. RESULTS: Six of seven dimensions of masculinity and the alcohol expectancy scales were significantly associated with both HED and negative consequences. In multivariate regression models predicting HED and negative consequences, the playboy and violence dimensions of masculinity and the risk/aggression alcohol expectancy remained significant. HED and the risk-taking dimension of masculinity were also significant in the model predicting negative consequences. The structural equation model indicated that masculinity was directly associated with HED and negative consequences but also influenced negative consequences indirectly through HED and alcohol expectancies. CONCLUSIONS: The findings suggest that, among young adult male college and university students, masculinity is an important factor related to both HED and drinking consequences, with the latter effect partly mediated by HED and alcohol expectancies. Addressing male norms about masculinity may help to reduce HED and negative consequences from drinking.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".