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Record W4396799877 · doi:10.1080/26895269.2024.2351472

The experiences of transgender and gender diverse children and youth using telehealth: A meta-ethnography

2024· article· en· W4396799877 on OpenAlexaff
Josh Vriends, Holly Symonds‐Brown

Bibliographic record

VenueInternational Journal of Transgender Health · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransgenderTelehealthEthnographyTransgender womenPsychologyGender studiesSociologyHuman immunodeficiency virus (HIV)MedicineTelemedicinePolitical scienceAnthropologyHealth careFamily medicineMen who have sex with men

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.206
GPT teacher head0.443
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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