The Role of Environmental Contexts on Impacts of Gender Minority Stress
Bibliographic record
Abstract
Purpose: Individuals who identify as transgender and gender diverse (TGD) often experience unique stressors associated with their TGD identity, referred to as gender minority stress (GMS), which can increase negative affect. This study examined whether social (i.e., when alone, with close others, with strangers/acquaintances) and environmental (i.e., at home, out-of-home) context was related to the likelihood of experiencing GMS and whether social and environmental context moderated the relationship between GMS and negative affect.Methods: This is a secondary analysis of ecological momentary assessment (EMA) data from 25 TGD adults who randomly received brief surveys twice daily for 21 days (N = 1,050 observations analyzed). Participants reported where they were, who they were with, GMS they recently experienced, and their current level of negative affect. Using multilevel models, we examined whether GMS was more likely to be experienced in specific contexts, whether GMS predicted negative affect, and if these contexts moderated the association between GMS and negative affect. Results: Participants were significantly more likely to experience a proximal gender minority stressor when they were out-of-the-home alone (compared to home alone), and when they were with non-close others (compared to close others). Social and environmental context significantly moderated the within-person association between GMS and negative affect. GMS was positively associated with negative affect for when participants were home alone but not when out-of-the-home alone. Conclusions: Social and environmental context may impact GMS experiences and subsequent negative affect, and should be considered in related interventions.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".