Associations between misgendering, invalidation, pride, community connectedness, and trauma among nonbinary adults.
Bibliographic record
Abstract
Transgender and nonbinary (TNB) people experience elevated rates of posttraumatic stress (PTS) due to transphobic violence, discrimination, microaggressions, and minority stress. Nonbinary people in particular experience unique chronic minority stressors (e.g., misgendering, interpersonal invalidation) because of the assumption that gender is inherently binary. Such examples of oppression against TNB people could contribute to complex PTS (c-PTS) symptoms, which arise due to exposure to chronic, cumulative, and interpersonal trauma. This study aimed to examine how misgendering and invalidation may be associated with PTS and c-PTS symptoms among nonbinary people and whether this association is moderated by pride and community connectedness. Cross-sectional data from 610 nonbinary people living in the United States and Canada were analyzed using hierarchical linear regressions. Misgendering and invalidation had significant positive associations with PTS and c-PTS symptoms. However, pride and community connectedness were not significant moderators of these associations. Findings from this study contribute to the conceptualizations of traumatic stress among nonbinary people and provide considerations for more affirming trauma-informed care. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".