Identifying protective factors for gender diverse adolescents’ mental health
Bibliographic record
Abstract
Few studies have disentangled differences in victimization exposures and mental health symptoms among gender diverse subgroups, nor considered the role of potential protective factors in ameliorating the impact of victimization on gender diverse youths' mental health. Here we report findings from a secondary data analysis, in which we address this gap by analyzing cross-sectional survey data (N = 11,264 in the final analytic sample) from a population-based survey of youth in participating school districts in a large Midwestern U.S. county. Relative to cisgender youth with gender conforming expression, transgender youth and cisgender youth with nonconforming gender expression are more likely to experience victimization and severe mental health concerns. Additionally, school-connectedness moderates the association between bias-based harassment and depression for cisgender youth with gender nonconforming expression, and family support/monitoring buffers the association of peer victimization with suicide attempts among transgender youth. Findings highlight the need to better understand factors which may confer protection among gender diverse adolescents, so that in turn appropriate supports across key contexts can be implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".