We’re in this together: LGBQ social identity buffers the homonegative microaggressions—alcohol relationship
Bibliographic record
Abstract
Research indicates that rates of alcohol use and alcohol consequences are higher among LGBQ emerging adults (EAs; ages 18–25) than among their heterosexual counterparts and this is partly due to experiences of sexual orientation-based discrimination. To date, however, there is limited research on factors that mitigate against increased alcohol outcomes among LGBQ EAs. The purpose of the current study was to examine the buffering effects of LGBQ social identity components (ingroup ties, centrality, ingroup affect) on the relationship between two types of discrimination (homonegative microaggressions and discrimination violence) and alcohol use and consequences. A community-based sample of 252 LGBQ EAs completed an online survey. There was a significant moderating effect for ingroup ties and ingroup affect where the relationship between homonegative microaggressions and alcohol use and consequences was lower for those higher on these social identity components; there was no moderating effect of any social identity component on the association between discrimination violence and either alcohol outcome. Social identity factors strongly affiliated with the LGBTQ community act as both a buffer in the face of subtle forms of discrimination and, more generally, a way to counteract the typical trajectory of increased alcohol use and consequences among LGBQ EAs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".