Bibliographic record
Abstract
Background: Many non-binary individuals AFAB (assigned female at birth) seek gestational parenthood. However, the limited available literature is often focused on trans men and overlooks the conception, pregnancy, and birth experiences of non-binary parents.Aims: The study aimed to capture the unique reproduction narratives of non-binary people AFAB.Methods: Five non-binary individuals volunteered to participate in this study. Data were collected using largely unstructured, in-depth, tape-recorded interviews. Thematic analysis of the verbatim transcripts and tape recordings yielded a chronological, cohesive narrative for each participant. Four participants reviewed their narrative and confirmed that their story was accurately represented. The individual narratives were then woven into one collective narrative, and common themes across the participants’ stories were identified.Results: Before conception, most participants considered how to balance their medical and social transitions with their reproductive goals. Conception was relatively easy and straightforward for the four participants who used their partner’s sperm. The gendered nature of, and language surrounding, pregnancy greatly impacted participant’s reproductive experiences, leading to feelings of isolation and loneliness, difficulties finding maternity clothes and gender dysphoria. Participants desired gender-affirming care and reported mostly positive experiences with their healthcare providers. Their gender identity influenced their experiences of parenthood, as well as the decisions they made regarding the disclosure of their gender identity to others, their gender presentation, chestfeeding, and parental designations.Discussion: The cisnormative and heteronormative scripts that surround pregnancy shaped the reproductive narratives of those who participated in this research. The findings reinforce the importance of inclusive, gender-affirming healthcare and social support services.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.003 | 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".