Reproductive Journeys: A Comparative Analysis of Patient-Centered Care and Well-Being in 2S/LGBTQIA+ and Mixed-Sex/Gender Couples Undergoing Medically Assisted Reproduction
Bibliographic record
Abstract
Quantitative studies on mixed-sex/gender couples show that infertility and medically assisted reproduction (MAR) impact psychological and relational well-being, emphasizing the need for patient-centered care. However, little is known about the experiences of 2S/LGBTQIA+ couples who primarily seek MAR due to social infertility. While qualitative findings suggest unique barriers to sensitive care, quantitative comparisons remain limited. This study compared personal and relational well-being, along with perceptions of patient-centered care, between 2S/LGBTQIA+ and mixed-sex/gender couples undergoing MAR. The sample included 345 Canadian and American couples (80 2S/LGBTQIA+, 265 mixed-sex/gender) recruited from fertility clinics and online platforms. Participants completed the Patient-Centredness Questionnaire-Infertility, Hospital Anxiety and Depression Scale, Brief Dyadic Adjustment Scale, and Dyadic Coping Inventory. Mixed-effect models adjusted for sociodemographic factors revealed no group differences in anxiety or perceived patient-centered care. However, mixed-sex/gender couples reported significantly higher depressive symptoms (p = .008, d = 0.33), while 2S/LGBTQIA+ couples showed higher relationship satisfaction (p = .004, d = -0.33) and greater use of common dyadic coping strategies (p = .016, d = -0.31). These findings highlight relational strengths among 2S/LGBTQIA+ couples despite potential access barriers. Future research should further explore diverse MAR experiences to inform inclusive, sensitive, and equitable fertility 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".