This Wasn’t a Split-Second Decision”: An Empirical Ethical Analysis of Transgender Youth Capacity, Rights, and Authority to Consent to Hormone Therapy
Bibliographic record
Abstract
Inherent in providing healthcare for youth lie tensions among best interests, decision-making capacity, rights, and legal authority. Transgender (trans) youth experience barriers to needed gender-affirming care, often rooted in ethical and legal issues, such as healthcare provider concerns regarding youth capacity and rights to consent to hormone therapy. Even when decision-making capacity is present, youth may lack the legal authority to give consent. The aims of this paper are therefore to provide an empirical analysis of minor trans youth capacity to consent to hormone therapy and to address the normative question of whether there is ethical justification for granting trans youth the authority to consent to this care. Through qualitative content analysis of interviews with trans youth, parents, and healthcare providers, we found that trans youth demonstrated the understandings and abilities characteristic of the capacity to consent to hormone therapy and that they did consent to hormone therapy with positive outcomes. Employing deontological and consequentialist reasoning and drawing on a foundation of empirical evidence, human rights, and best interests we conclude that granting trans youth with decisional capacity both the right and the legal authority to consent to hormone therapy via the informed consent model of care is ethically justified.
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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 | 0.055 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".