“I have been thinking about this for a long time”: Navigating gender affirming medical care decisions for trans and nonbinary youth and their families in six countries
Bibliographic record
Abstract
Background: It is frequently reported that access to gender affirming medical care (GAMC) is burdened with many barriers, such as lack of parental support, geographic distance, absence of social support, and medical professionals' lack of knowledge. While the current knowledge shows that trans and nonbinary youth (TNBY) experience many challenges when accessing GAMC, there is still limited information available regarding how they navigate GAMC decisions as youth approach puberty. Methods: To fulfill that gap, a team of researchers conducted semi-structured interviews in six countries (Australia, Canada, India, Switzerland, the UK, and the US) with TNBY ages 8-14 years and their families. Results: Thematic analysis was conducted on baseline interview data from an international longitudinal study, yielding three themes, and subthemes, relevant to making GAMC decisions: (1) Participants' decisions guided by expectations, (2) conditions for reaching an informed decision, and (3) the role of time in GAMC decision-making. Of these themes, several subthemes allowed researchers to understand how families navigate whether to seek GAMC, and if so, deciding on specific care trajectories. Conclusion: Results indicated that making decisions regarding GAMC is a process that moves beyond medical impacts and side-effects, as many family dynamics, access to information and time also played into TNBY's decisions to seek GAMC.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".