The experiences of transgender and gender diverse children and youth using telehealth: A meta-ethnography
Bibliographic record
Abstract
Background Transgender and gender diverse (TGD) children and youth face significant disparities when accessing healthcare. Telehealth has become a promising strategy for improving healthcare access. The experiences of TGD children and youth using telehealth to access healthcare are poorly understood.Aim To synthesize the current evidence on TGD children and youths’ experiences using telehealth.Methods A meta-ethnography was conducted on seven papers examining TGD children and youths’ experiences with telehealth.Results The main findings expressed by TGD children and youth regarding their experiences of telehealth encompassed the themes of feeling safe, feeling seen, ease of access, and technological affordances.Discussion We propose a model to consider when designing telehealth for TGD children and youth entitled Trans-IT, incorporating the four key themes: feeling safe, feeling seen, ease of access, and technological affordances. Overall, this study identifies the range of user experiences that influence the accessibility and relevance of care available through telehealth for TGD children and youth and provides a foundation for future policy, practice, and research.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".