Gaps in Severe Maternal Morbidity and the Lack of Preconception Care Between Non-Hispanic Black Women and Non-Hispanic White Women: A U.S Health Crisis
Bibliographic record
Abstract
Improving preconception care in the United States will reduce the race-based health disparity that exists between Non-Hispanic Black women and Non-Hispanic White women regarding severe maternal morbidity (SMM). This opinion article describes the reasons why preconception care with in the U.S. should be improved to reduce the rates of SMM among women. By promoting reproductive planning and contraceptive use along with the implementation of chronic disease management into clinical practice, efforts to prioritize preconception care will be successful. Prioritizing preconception care in these ways will aid in the successful transformation of health care delivery provided to Non-Hispanic Black women who are at a greater predisposed risk to develop SMM compared to their Non-Hispanic White counterparts. An additional factor presented considers how the lack of access to healthcare, which predominantly affects Non-Hispanic Black women, prevents this group of women from accessing preconception care to reduce rates of SMM. By implementing preconception care methods, some may argue that the disparity gap will ultimately widen due to the differential health-care access between Non-Hispanic Black women and Non-Hispanic White women. This argument is further considered and a possible solution to this problem is provided.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".