Predictors and Mental Health Benefits of Chosen Name Use Among Transgender Youth
Bibliographic record
Abstract
Chosen name use among transgender youth (youth whose gender identities are different than their sex assigned at birth) can be part of the complex process of aligning gender presentation with gender identity and can promote mental health. However, little is known about the factors that predict whether or not transgender youth have a chosen name and outcomes of chosen name use, especially in specific social contexts. We examined, among a sample of 129 transgender youth from three cities in the United States, differences in sociodemographic characteristics and mental health outcomes between transgender youth with and without a chosen name and, among those with a chosen name, predictors and mental health benefits of being able to use a chosen name at home, school, and work. There were few differences between transgender youth with and without a chosen name. Among transgender youth with a chosen name, disclosure of gender identity to supportive family and teachers predicted chosen name use at home and school, respectively. Chosen name use was associated with large reductions in negative health outcomes and relatively smaller improvements in positive mental health outcomes. Our results show that chosen name use is part of the gender affirmation process for some, but not all, transgender youth and is associated with better mental health among transgender youth who adopt a chosen name.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".