Experiences and Interactions with the Healthcare System in Transgender and Non-Binary Patients in Austria: An Exploratory Cross-Sectional Study
Bibliographic record
Abstract
Medical care of transgender and non-binary (TNB) patients if often a complex interdisciplinary effort involving a variety of healthcare workers (HCWs) and services. Physicians not only act as gatekeepers to routine or transitioning therapies but are also HCWs with the most intimate and time-intensive patient interaction, which influences TNB patients' experiences and health behaviors and healthcare utilization. The aim of this study was to investigate the physician-patient relationship in a sample of TNB individuals within the Austrian healthcare system, and explore its associations with sociodemographic, health-, and identity-related characteristics. A cross-sectional study utilizing an 56-item online questionnaire, including the Patient-Doctor Relationship Questionnaire 9 (PDRQ-9), was carried out between June and October 2020. The study involved TNB individuals 18 or older, residing in Austria, and previously or currently undergoing medical transition. In total, 91 participants took part, of whom 33.0% and 25.3% self-identified as trans men and trans women, respectively, and 41.8% as non-binary. Among participants, 82.7% reported being in the process of medical transitioning, 58.1% perceived physicians as the most problematic HCWs, and 60.5% stated having never or rarely been taken seriously in medical settings. Non-binary participants showed significantly lower PDRQ-9 scores, reflecting a worse patient-physician relationship compared to trans male participants. TNB patients in Austria often report negative experiences based on their gender identity. Physicians should be aware of these interactions and reflect potentially harmful behavioral patterns in order to establish unbiased and trustful relations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".