“<i>Doctors asked if we are sisters or friends</i>”: Experiences of 2S/LGBTQIA+ couples in the context of medically assisted reproduction
Bibliographic record
Abstract
Although 20% of couples who seek medically assisted reproduction (MAR) identify as 2S/LGBTQIA+, MAR services are primarily based on a medical/cisgender definition of infertility, failing to account for 2S/LGBTQIA+ individuals’ experience of social infertility—that is, their inability to conceive due to their relationship status. Whereas the consequences of the MAR process on various aspects of mixed-gender/sex couples have been demonstrated (e.g., emotional, relationship, sexuality, social life), the generalization of this knowledge to the 2S/LGBTQIA+ community remains unexplored. This qualitative study aimed to explore the specific impacts of MAR on different aspects of 58 Canadian 2S/LGBTQIA+ couples’ lives. While participants were asked about the impact of their MAR journey on the various spheres of their lives, the thematic analysis revealed that what proved most central to their experience were the barriers they encountered to access sensitive and inclusive care, echoing themes from existing literature such as heteronormativity and cisnormativity, a lack of tailored services, psychological distress triggered by the MAR process, and experiences of stigma and discrimination. Other themes outside the healthcare context were also identified: financial burden, a lack of social models and support systems, the emotional toll of repeatedly coming out, and microaggressions from family members. These findings underscore the urgent need for targeted research and reforms in reproductive healthcare to better serve 2S/LGBTQIA+ couples and address the systemic barriers they face.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".