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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".