Usability of Virtual Visits for the Routine Clinical Care of Trans Youth during the COVID-19 Pandemic: Youth and Caregiver Perspectives
Bibliographic record
Abstract
We evaluated families’ perspectives on the usability of virtual visits for routine gender care for trans youth during the COVID-19 pandemic. An online survey, which included a validated telehealth usability questionnaire, was sent to families who had a virtual Gender Clinic visit between March and August 2020. A total of 87 participants completed the survey (28 trans youth, 59 caregivers). Overall, usability was rated highly, with mean scores between “quite a bit” and “completely” in all categories (usefulness, ease of use, interface and interaction quality, reliability, and satisfaction). Caregivers reported higher usability scores compared to trans youth [mean (SD) 3.43 (0.80) vs. 3.12 (0.93), p = 0.01]. All families felt that virtual visits provided for their healthcare needs. A total of 100% of youth and caregivers described virtual appointments as safer or as safe as in-person visits. A total of 94% of participants would like virtual visits after the pandemic; families would choose a mean of two virtual and one yearly in-person visit with a multidisciplinary team. Overall, virtual gender visits for trans youth had impressive usability. Participants perceived virtual visits to be safe. For the future, a combination of virtual and in-person multidisciplinary visits is the most desired model.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".