The Relationship Between Bias-Related Victimization and Generalized Anxiety Disorder Among American Indian and Alaska Native Lesbian, Gay, Bisexual, Transgender, Two-Spirit Community Members
Bibliographic record
Abstract
Lesbian, gay, bisexual, transgender, two-spirit, and American Indian and Alaska Native community members share long histories of discrimination and poorer health status as compared to mainstream Americans. In particular, these groups experience bias-related victimization, a type of discrimination based on inherent traits such as race or ethnicity and sexual orientation. This cross-sectional study (N = 334) used a revised bias-related victimization measure and examined the relationship between self-reported bias-related victimization and generalized anxiety disorder, depression, and substance abuse among lesbian, gay, bisexual, transgender, and two-spirit American Indians and Alaska Natives. The results showed that 84.4% reported experiencing bias-related victimization. Those with the highest levels of bias-related victimization had 2.79 times (p = .009; 95% CI [1.30, 6.02]) the risk of reporting symptoms of generalized anxiety disorder as compared to those with no bias-related victimization, controlling for income, education, sex, age, sexual orientation, and chronic disease. There was no significant relationship between bias-related victimization and major depression or substance dependence/abuse. Our results support a potential relationship between bias-related victimization and generalized anxiety disorder for lesbian, gay, bisexual, transgender, and two-spirit American Indians and Alaska Natives. Including diverse populations in research is essential to a better understanding of the impact on health outcomes. Inclusion of bias-related victimization questions in clinical treatment may help identify at-risk patients.
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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 | 0.000 | 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) | 0.001 | 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".