Bibliographic record
Abstract
A wide range of terms are used to describe gender-diverse people, including transgender, gender-fluid, gender-queer, non-binary, and two‑spirit, reflecting the diversity of the community itself. Transgender and gender-diverse patients (TGDP) may experience gender dysphoria—the distress that arises when their gender identity does not align with the sex assigned at birth. TGDP are estimated to represent between 0.1% and 2% of the global population; in Canada, the 2019 census reported a prevalence of 0.35%. Access to gender-affirming care is strongly linked to improved health outcomes. One study found that suicidal ideation decreased from 67% prior to transition to just 3% afterward. Yet, despite the clear benefits, TGDP continue to face major barriers to care. According to the Trans PULSE survey, as of 2019, only 35% of respondents had completed their medical transition. Even in general healthcare, access remains inequitable: while 81% of respondents reported having a primary care provider (PCP), only 52% felt comfortable discussing trans‑related health issues with their PCP, and over 40% reported having an unmet healthcare need. These disparities reflect the ongoing impact of transphobia and prior trauma within healthcare systems, and as a result, TGDP face disproportionate health burdens compared to the general population, including lower rates of cancer screening, higher rates of mental health disorders, and sexually transmitted infections (Figure 1). Addressing these inequities requires urgent action to expand access to gender-affirming hormone therapy, surgery, and mental health care. Equally important, healthcare systems must adopt inclusive policies and practices that improve access to all forms of care for TGDP. This article outlines practical measures that any healthcare practice can implement to create a more welcoming and affirming environment.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.168 | 0.078 |
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".