Masculine and feminine orientations on emotions and alcohol use in response to romantic conflict
Bibliographic record
Abstract
This research examines associations of gender role (i.e., masculine and feminine) orientations with drinking alcohol shortly after a recent significant romantic relationship conflict, and whether experiencing specific emotions in response to the conflict has indirect effects on gender role orientation effects on drinking. Participants included 410 American and Canadian adults (59% female) in committed romantic relationships who consumed alcohol regularly (i.e., 12+ drinks/past year). Participants completed an online survey and reported their biological sex, gender identity, gender role orientation, the degree to which they experienced a variety of negative (e.g., ashamed, angry) and positive emotions (e.g., validated, connected) in response to a recent significant romantic relationship conflict, and the amount of alcohol consumed following the conflict. Zero-inflated negative binomial models indicated masculine orientation was related to greater odds of drinking post-conflict. Masculine orientation was associated with experiencing more negative affect and emotions such as feeling sad, disgusted, and powerless. Feminine orientation was associated with positive affect and emotions such as understood, connected, and happy. Experiencing negative emotions was linked with greater odds of drinking and drinking more post-conflict. Analyses of indirect effects indicated that overall negative affect and all individual negative emotions explained links of masculine orientation with drinking whereas feminine orientation was indirectly associated with drinking via both overall negative and positive affect as well as several self-conscious, anxious, and positive emotions. Our study clarifies the emotions that should be targeted in individuals with specific gender role orientations to prevent excessive drinking and/or relapse following romantic conflict.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".