Testing whether the combination of victimization and minority stressors exacerbate PTSD risks in a diverse community sample of sexual minority women
Bibliographic record
Abstract
Informed by minority stress and intersectionality frameworks, we examined 1) associations of sexual identity and race/ethnicity with probable diagnosis of post-traumatic stress disorder (PTSD-PD) among sexual minority women (SMW; e.g. lesbian, bisexual) and 2) potential additive and interactive associations of minority stressors (discrimination, stigma consciousness and internalised homonegativity) and potentially traumatic childhood and adulthood events (PTEs) with PTSD-PD. The data come from a large and diverse community sample in the United States of SMW (N=662; age range: 18–82; M=40.0, SD=14.0). The sample included 35.8% Black, 23.4% Latinx and 37.2% White participants. More than one-third of SMW (37.2%) had PTSD-PD with significantly higher prevalence among bisexual, particularly White bisexual women, than lesbian women. Discrimination, stigma consciousness and internalised homonegativity were each associated with higher odds of PTSD-PD, but only internalised homonegativity was additively associated with PTSD-PD above and beyond effects of PTEs. We found no evidence for interactive effects between PTEs and minority stressors. PTSD was strongly associated with childhood PTEs and with minority stressors above and beyond associations with adulthood PTEs and stressors. Our findings suggest a strong need to address effects of marginalization in treatment for PTSD, as minority stressors likely maintain and exacerbate effects of past traumas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".