Health Disparities in Transgender and Gender Expansive Adolescents: A Topical Review From a Minority Stress Framework
Bibliographic record
Abstract
OBJECTIVE: To present a topical review of minority stressors contributing to psychosocial and physical health disparities in transgender and gender expansive (TGE) adolescents. METHODS: We conducted a topical review of original research studies focused on distal stressors (e.g., discrimination; victimization; rejection; nonaffirmation), proximal stressors (e.g., expected rejection; identity concealment; internalized transphobia), and resilience factors (e.g., community connectedness; pride; parental support) and mental and physical health outcomes. RESULTS: Extant literature suggests that TGE adolescents experience a host of gender minority stressors and are at heightened risk for negative health outcomes; however, limited research has directly applied the gender minority stress framework to the experiences of TGE adolescents. Most research to date has focused on distal minority stressors and single path models to negative health outcomes, which do not account for the complex interplay between chronic minority stress, individual resilience factors, and health outcomes. Research examining proximal stressors and resilience factors is particularly scarce. CONCLUSIONS: The gender minority stress model is a helpful framework for understanding how minority stressors contribute to health disparities and poor health outcomes among TGE adolescents. Future research should include multiple path models that examine relations between gender minority stressors, resilience factors, and health outcomes in large, nationally representative samples of TGE adolescents. Clinically, adaptations of evidence-based interventions to account for gender minority stressors may increase effectiveness of interventions for TGE adolescents and reduce health disparities in this population of vulnerable youth.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".