Mental Health and Victimization Among <scp>LGBTQ</scp>+ Youth: Future Directions for Support in Schools
Bibliographic record
Abstract
T he impact of the SARS-CoV-2 (COVID-19) pan- demic on the United States (U.S.) education system coupled with the proliferation of anti-lesbian, gay, bisexual, transgender, and queer (LGBTQ+) bills, discrimination and racism has been significant and farreaching-further exacerbating existing mental health disparities and inequities that marginalized youth experience.[1][2][3][4][5][6][7][8][9] Discrimination and racial bias, intersecting with gender bias, can begin as early as preschool and continue to college admissions (as seen by the recent reversal of collegiate Affirmative Action programs by the U.S. Supreme Court).4,5 In addition, 520 anti-LGBTQ+ bills have been introduced across the U.S. in 2023 alone, which is a new record. 1 Many of these bills specifically target bans or restrictions to gender-affirming health care for youth, resources and opportunities for trans students (eg, sports participation, updating gender marker and name in school records, use of pronouns, and undermining the privacy of trans status disclosure to parents), book bans, and
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 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 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 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".