Development and Validation of the Nonbinary Distal Minority Stressors, Proximal Minority Stressors, and Resilience Scales
Bibliographic record
Abstract
Nonbinary populations face considerable mental health disparities likely due to their experiences of minority stress. Nonbinary people face similar minority stressors as trans men and trans women, but they also face unique stressors due to living in a world structured around the gender binary. Although validated measures exist that measure minority stress and resilience among trans and nonbinary people broadly (e.g., Testa et al., 2015), to date, no validated measures exist that capture the unique minority stress and resilience experiences of nonbinary people. Our study aimed to develop and validate three scales: the Nonbinary Distal Minority Stressors Scale (Nbi-DMSS), the Nonbinary Proximal Minority Stressors Scale (Nbi-PMSS), and the Nonbinary Resilience Scale (Nbi-RS). We recruited a large, racially diverse sample of nonbinary adults (N = 611) who live in the U.S. or Canada. Results showed that all measures have strong structural, convergent, discriminant, and criterion-related validity and that the scales and their subscales are reliable. Invariance testing found that the scales were valid across race, assigned sex, and age cohorts. Our study also advances minority stress theory by presenting the nonbinary minority stress and resilience model, which includes unique nonbinary minority stressors such as invalidation, burdening, binary normativity, and mental and emotional labor, and unique nonbinary resilience factors such as gender validation and critical consciousness. The nonbinary minority stress and resilience model and scales can advance research and clinical work to support the unique needs of nonbinary populations.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".