O-157 Beyond medical infertility: a mixed-methods study of 2S/LGBTQIA+ couples undergoing medically assisted reproduction
Bibliographic record
Abstract
Abstract Study question How do 2S/LGBTQIA+ and mixed-sex/gender couples undergoing medically assisted reproduction (MAR) compare in terms of psychological, relational well-being and perceptions of patient-centered care? Summary answer 2S/LGBTQIA+ couples reported systemic barriers in MAR but demonstrated higher relational well-being and lower depressive symptoms than mixed-sex/gender couples. What is known already Although 20% of MAR-seeking couples identify as 2S/LGBTQIA+, fertility care remains rooted in a medicalized, cisgender framework of infertility. This approach disregards social infertility, where conception barriers stem from relationship structure rather than medical causes. While extensive research has examined MAR’s psychological and relational impact on mixed-sex/gender couples, little is known about its effects on 2S/LGBTQIA+ couples. Prior studies have compared lesbian and heterosexual couples on relational satisfaction, but none have systematically assessed the individual, relational, and contextual well-being of the broader 2S/LGBTQIA+ population undergoing MAR for social infertility. The impact of MAR-related stressors on their overall experience remains underexplored. Study design, size, duration This mixed-method study, part of a larger longitudinal project, included 345 couples (80 2S/LGBTQIA+ and 265 mixed-sex/gender) recruited between November 2019 and April 2024 across Canada and the United States. A subsample of 58 2S/LGBTQIA+ couples also answered a single open-ended item about their experience in fertility clinics. Their responses were used for a qualitative analysis. Participants/materials, setting, methods Couples seeking MAR within six months of their initial clinic visit were recruited via social media and affiliated clinics. Both partners completed online questionnaires assessing sociodemographics, medical history, psychological and relationship functioning, and perceptions of patient-centered care. A single open-ended item allowed 2S/LGBTQIA+ participants to describe their clinic experiences. The 2S/LGBTQIA+ group included couples with (a) same-gender partners and/or (b) one or both partners identifying with a gender different from their sex assigned at birth. Main results and the role of chance Quantitative findings revealed no significant differences between 2S/LGBTQIA+ and mixed-sex/gender couples in anxiety symptoms or in the communication and respect domains of patient-centered care. However, mixed-sex/gender couples reported significantly higher depressive symptoms (F(1, 205.347) = 7.123, p = .008, d = 0.34). In contrast, 2S/LGBTQIA+ couples exhibited significantly greater relationship satisfaction (F(1, 212.442) = 8.288, p = .004, d = -0.39) and more frequent use of common dyadic coping strategies (F(1, 206.457) = 5.929, p = .016, d = -0.35). Qualitative findings contextualized these results by identifying pervasive barriers in MAR for 2S/LGBTQIA+ couples and thereby suggesting that, while both groups presented similar rates of anxious symptoms, different underlying process could be at play. Indeed, participants reported challenges including heteronormativity, financial strain, lack of inclusive services, and the emotional toll of repeated identity disclosures. Microaggressions from healthcare providers and family members further exacerbated stress. However, many couples highlighted the strength of their relational bonds and reliance on social support networks as key coping mechanisms. These findings suggest that future studies should delve deeper into these dynamics, focusing on the specific stressors and relational strengths that characterize different populations within the MAR context. Limitations, reasons for caution The study’s cross-sectional design limits causal interpretations. The sample lacked racial and educational diversity, restricting generalizability. Male same-sex/gender couples were underrepresented, and intersectional factors (e.g., race, socioeconomic status) were not examined. Future research should investigate subgroup-specific experiences and longitudinal changes across different MAR stages. Wider implications of the findings This is the first study to quantitatively compare 2S/LGBTQIA+ and mixed-sex/gender couples undergoing MAR, encompassing diverse gender and sexual identities. Findings highlight the urgent need for inclusive reproductive healthcare, policy adaptations, and tailored mental health support to better address the unique experiences faced by diverse family formations. Trial registration number No
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".