Black Mothers in America
Bibliographic record
Abstract
The United States has the highest rate of maternal mortality among high-income countries, despite spending the single-largest percentage of GDP on healthcare. This burden disproportionately afects Black mothers who experience a maternal mortality ratio that is four times that of White mothers. Tis case study demonstrates that the disparities in maternal outcomes between Black and White mothers are rooted in racial discrimination. This racial inequality manifests in part through increased allostatic load as a result of intergenerational experiences of racism; unequal access to high quality insurance coverage; and racial discrimination by healthcare practitioners. Potential interventions to explore include federal and state employment regulations that lessen the socioeconomic barriers preventing Black Americans from accessing quality insurance coverage; crosscultural training programs in healthcare facilities and teaching institutions; and a systematic shif toward holistic models of childbirth. Tough these interventions can serve to diminish the consequences felt on the individual level, collaborative multi-systemic change is necessary to address the social determinants of health that result in poor maternal outcomes on a national level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".