Perceptions of the Black population on fertility care: A scoping review
Bibliographic record
Abstract
• Black women experience poorer IVF outcomes compared to White women. • Anatomical and psychosocial factors contribute to IVF outcome disparities. • Barriers include discrimination, lack of knowledge, cultural insensitivity, & cost. • Facilitators include education on insurance coverage & understanding of IVF plan. • Addressing financial & cultural barriers is key to improving Black women’s IVF care. Assisted reproductive technologies (ART), such as in vitro fertilization (IVF), offer hope for people struggling with infertility. Studies demonstrate that disparities in access and experiences with fertility care exist among different racial groups, particularly affecting Black women. This scoping review aims to explore Black women’s perceptions of fertility care and psychosocial barriers that they encounter when accessing care. This scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Checklist. Three databases including OVID Medline, PubMed, and Embase, were searched using keywords that resulted in 12 studies that explored barriers to fertility care for Black women. Identified barriers included discrimination, lack of knowledge, and a lack of cultural sensitivity among physicians. The financial cost of treatment was the most frequently reported barrier to infertility treatment. Identified facilitators to infertility treatment included a thorough understanding of the treatment plan and procedures, culturally competent providers, and fertility counseling for patients including education on existing insurance coverage for infertility treatments. Recognizing disparities in IVF outcomes, and identifying both barriers and facilitators to treatment, is an essential first step toward improving fertility care outcomes.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".