Assessing the Influence of Stigma and Discrimination on Barriers to Health Care Access in Transgender Populations
Bibliographic record
Abstract
Introduction/Background: The current pace of change in the recognition, awareness, and understanding of transgender issues have encouraged more individuals to acknowledge and accept their gender identities today than in any previous generation (1). Despite recent progress in the rights of sexual and gender minorities, health care systems and providers around the world are struggling to respond to this unprecedented rise in patients presenting with gender dysphoria. As a result, there has been an overall failure in adequately caring for transgender individuals, leading them to face significant barriers and challenges in their access to health care.Findings: Considerable evidence has shown that transgender patients perceive substantial discrimination in health care settings (2). Most health care providers have minimal experience interacting with a diverse patient population and are largely unaware of sexual minority health issues and terminology. Recent studies have demonstrated that providers generally feel unprepared to offer quality care to transgender patients, illustrating a significant gap in the medical curriculum and training of these professionals (2). Beyond this lack of clinical competence, transgender individuals are often severely mistreated by medical providers (3). In a recent study of discrimination and health care experiences of LGBT patients in the United States, 20.9% of transgender patients reported being subjected to harsh language by a health care provider and 15% stated that their provider refused to touch them. 20.3% also reported being blamed for their own health problems and more than 25% stated that they were blatantly denied care due to their identity (4). Even when they are offered care, gender dysphoria is often treated as a psychiatric illness, rather than a matter of diversity. Constituting a form of unjust discrimination and a violation of self-determination, transgender patients are compelled to undergo mental assessments before pursuing gender affirmation surgeries (5).Conclusions: Given this discrimination, transgender populations are less likely than the general population to seek primary care and attention for life-threatening issues, causing them to suffer poorer health outcomes. As a group that is also unequally affected by significant psychological distress, debilitating depression, substance abuse, sexually transmitted infections and high suicide rates, it is imperative that these health care disparities be addressed (2).
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".