Men, trans/masculine, and non-binary people’s experiences of pregnancy loss: an international qualitative study
Bibliographic record
Abstract
BACKGROUND: Growing numbers of men, trans/masculine, and non-binary people are becoming gestational parents, yet very little is known about experiences of pregnancy loss among this diverse population. METHODS: The study employed a cross sectional design. Interviews were undertaken with a convenience sample of 51 trans/masculine and non-binary people who had undertaken at least one pregnancy, living in either Australia, the United States, Canada, or the European Union (including the United Kingdom). Participants were recruited by posts on Facebook and Twitter, via researcher networks, and by community members. 16 (31.2%) of the participants had experienced a pregnancy loss and are the focus of this paper. Thematic analysis was used to analyse interview responses given by these 16 participants to a specific question asking about becoming pregnant and a follow up probe question about pregnancy loss. RESULTS: Thematic analysis of interview responses given by the 16 participants led to the development of 10 themes: (1) pregnancy losses count as children, (2) minimizing pregnancy loss, (3) accounting for causes of pregnancy loss, (4) pregnancy loss as devastating, (5) pregnancy loss as having positive meaning, (6) fears arising from a pregnancy loss, (7) experiences of hospitals enacting inclusion, (8) lack of formal support offered, (9) lack of understanding from family, and (10) importance of friends. CONCLUSIONS: The paper concludes by outlining specific recommendations for clinical practice. These include the importance of focusing on the emotions attached to pregnancy loss, the need for targeted support services for men, trans/masculine, and non-binary people who undertake a pregnancy (including for their partners), and the need for ongoing training for hospital staff so as to ensure the provision of trans-affirming medical care.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".