Trans‐inclusive genetic counseling services: Recommendations from members of the transgender and non‐binary community
Bibliographic record
Abstract
The term transgender is used to describe individuals whose gender identity does not align with their sex assigned at birth. The term transgender can include individuals who identify as men, as women, as both of the traditional concepts of masculine and feminine gender, or neither masculine or feminine. The latter two gender identities may also be known as non-binary or gender non-conforming identities. Transgender individuals may attend genetic counseling for a variety of reasons, but current pedigree nomenclature does not adequately represent both sex assigned at birth (which is important for determining risk for certain conditions) and gender (which is important for providing trans-inclusive care). We conducted an interpretive description (qualitative, interview based) study with individuals from the transgender community to gather insight on pedigree nomenclature and more broadly, how to provide safe and effective genetic counseling for transgender individuals. We conducted semi-structured telephone interviews with individuals who identified as transgender or gender non-binary, transcribed them verbatim, and checked them for accuracy before coding and inductively identifying themes. Among our eight participants, five identified as trans-masculine, two as trans-feminine, five as non-binary/gender non-conforming (some participants had more than one gender identity). From the interviews, we identified a single key, over-arching theme: participants' felt it is the genetic counselor's responsibility to create safety and provide clarity about the clinical importance of both sex assigned at birth and gender identity for trans patients. Two specific strategies that counselors could use to achieve this safety and clarity were discussed extensively: (a) validating gender identity and (b) using inclusive and well-defined pedigree symbols that denote both sex assigned at birth and gender identity. Our data have important practice implications in terms of the importance of validating gender identity and using respectful pedigree symbols.
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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 | 0.036 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".