“It makes such a difference”: An examination of how LGBTQ youth talk about personal gender pronouns
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The World Health Organization has developed standards for youth-friendly health services to support adolescents and encourage health care utilization among youth. Necessary building blocks for youth-friendly care include strong interpersonal relationships between youth and health care providers. Nurse practitioners (NPs) may be particularly well positioned to form these relationships. This study explored a core aspect for building youth-provider relationships. The study examined how lesbian, gay, bisexual, transgender, and queer (LGBTQ) adolescents discussed use of personal pronouns (e.g., he, she, they, ze) in relation to transgender and gender diverse (TGD) people. METHODS: A secondary qualitative analysis of 66 in-depth interviews with LGBTQ youth from across Minnesota, Massachusetts, and British Columbia, Canada was conducted. Results were sorted into four main themes describing different aspects of personal pronoun use as related to TGD individuals. CONCLUSIONS: Stories and experiences shared by participants illustrate how to assess which pronouns to use for a given person, how to use pronouns in different contexts, why respecting pronouns is important to TGD people, and flexibility as an integral component of the learning process when it comes to appropriate pronoun use. IMPLICATIONS FOR PRACTICE: Understanding how youth discuss personal pronouns could improve practice with TGD youth. Each of the four themes can be applied to clinical encounters to ensure culturally sensitive care. Practice recommendations include asking adolescents what pronouns they prefer clinic staff to use on intake forms and having NPs and clinic staff provide their own pronouns to patients in introductions.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".