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Record W4413238244 · doi:10.1080/29968992.2025.2544325

Medical and cosmetic intervention needs, priorities and barriers of trans and non-binary youth in Quebec

2025· article· en· W4413238244 on OpenAlexaffabout
Pullen Sansfaçon Annie, G.A. Galkina

Bibliographic record

VenueInternational journal of LGBTQ+ youth studies. · 2025
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGender dysphoriaPsychological interventionTransgenderIntervention (counseling)MedicinePopulationFamily medicinePsychologyEnvironmental healthMedical educationNursing

Abstract

fetched live from OpenAlex

Trans and non-binary (TNB) youth aged 12–17 and 16–25 represent 0.2% and 0.79% of the Canadian population respectively, likely underestimated. TNB youth face mental health challenges, sometimes related to gender dysphoria, which can be significantly improved with gender-affirming interventions. However, the needs of TNB youth are poorly documented in Quebec (Canada). This study aims to understand the gender-affirming intervention needs and desires of TNB youth in Quebec. An online survey was conducted between April and June 2023, including open-ended questions. Descriptive analyses were performed. A total of 84 TNB youth from Quebec aged 15–24 completed the survey (40% transmasculine, 20% transfeminine and 39% non-binary). The most desired intervention was hormone therapy (95%). We found gendered differences in needs, particularly for facial and upper/lower body interventions. Our findings suggest that the needs of TNB youth vary according to gender. Inequitable financial barriers persist in covered gender-affirming medical care (GAMC), disadvantaging transfeminine youth. Our data also highlighted the importance of hormone treatments for TNB youth. In conclusion, it is essential to support TNB youth by considering their needs for GAMC, offering information on all available interventions, and ensuring equitable coverage to GAMC.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.027
GPT teacher head0.387
Teacher spread0.360 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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