Acceptance, Use, and Barriers of Telemedicine in Transgender Health Care in Times of SARS-CoV-2: Nationwide Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: The global incidence in the treatment of transgender people is increasing. During the COVID-19 pandemic, many consultations had to be cancelled, postponed, or converted to a virtual format. Telemedicine in the management of transgender health care could support physicians. OBJECTIVE: The aim of this study was to analyze the acceptance, use, and barriers of telemedicine in transgender health care in times of SARS-CoV-2 in Germany. METHODS: This prospective cross-sectional study was based on a survey of gynecological endocrinologists and transgender patients undergoing gender-affirming hormone treatment in Germany during the COVID-19 pandemic. Descriptive statistics were calculated, and regression analyses were performed to show correlations. RESULTS: We analyzed responses of 269 transgender patients and 202 gynecological endocrinologists treating transgender patients. Most believed that telemedicine was useful. Physicians and patients rated their knowledge of telemedicine as unsatisfactory. The majority of respondents said they did not currently use telemedicine, although they would like to do so. Patients and physicians reported that their attitudes toward telemedicine had changed positively and that their use of telemedicine had increased due to COVID-19. The majority in both groups agreed on the implementation of virtual visits in the context of stable disease conditions. In the treatment phases, 74.3% (150/202) of the physicians said they would use telemedicine during follow-ups. Half of the physicians said they would choose tele-counseling as a specific approach to improving care (128/202, 63.4%). Obstacles to the introduction of telemedicine among physicians included the purchase of technical equipment (132/202, 65.3%), administration (124/202, 61.4%), and poor reimbursement (106/202, 52.5%). CONCLUSIONS: Telemedicine in transgender health care found limited use but high acceptance among doctors and patients alike. The absence of a structured framework is an obstacle for effective implementation. Training courses should be introduced to improve the limited knowledge of physicians in the use of telemedicine. More research in tele-endogynecology is needed. Future studies should include large-scale randomized controlled trials, economic analyses, and the exploration of user preferences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".