Impacts of anti-trans discourses on gender-affirming care access in Brazil: family and provider views
Bibliographic record
Abstract
Abstract Over the past decade, professional associations worldwide have endorsed gender-affirming care (GAC) for transgender and gender-diverse (TGD) youth, based on growing evidence of its benefits for mental health and well-being. Nonetheless, GAC has become a controversial topic, with rising anti-trans discourse targeting families, providers, and youth. To date, little research has explored the impact of these movements on access to care-especially in Latin America. This research examines two questions: (1) How does anti-trans discourse manifest in the experiences of parents of TGD youth and providers working with GAC? (2) How does it impact GAC access and provision? Data comes from an ethnographic study conducted at a transgender health care facility in a major Brazilian city (2023-2024), including 28 interviews (13 with family members of TGD youth ages 4-17 and 14 with providers) and 18 months of participant observation. A thematic analysis was conducted using a constructivist and interpretative lens. Findings show that anti-trans discourse is present in daily life, spread by professionals, institutions, and policymakers. It undermines the validity of transgender identity by reinforcing congruence between sex and gender, while discouraging affirming attitudes toward other diversities. For parents, it (i) makes it harder to challenge beliefs about their child's identity as a phase, (ii) increases guilt by linking gender nonconformity to poor parenting, and (iii) amplifies fear of regret. For providers, it (i) undermines trust in clinical assessments, (ii) reduces support from colleagues, and (iii) exposes services to scrutiny. Overall, anti-trans discourse fosters uncertainty, a sense of persecution, and hesitancy-affecting the ability to perceive needs, seek or provide care, and engage with GAC. Findings highlight how anti-trans narratives can erode support systems and shape health outcomes. Key messages • The research shows that anti-trans discourse has become a public health issue undermining the ability to seek and receive adequate support for families and youth. • There’s growing importance in opposing anti-trans discourse, particularly by protecting specialized services and increasing support for families and youth.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".