Preterm and low birthweight birth in the United States: Black midwives speak of causality, prevention, and healing
Bibliographic record
Abstract
BACKGROUND: Low birthweight (LBW) and preterm birth (PTB) are more common among Black infants than white infants in the United States. Although multiple hypotheses have been proposed to explain elevated rates of PTB and LBW, the perspectives of Black midwives who serve Black communities are largely missing from the literature. METHODS: Using semi-structured interviews and focus groups with a purposive sample of midwives (n = 29), we elicited midwives' perceptions of PTB and LBW causation, as well as insights on culturally congruent strategies for prevention. We used consensus coding and reciprocal ethnography to increase the rigor of our analyses. RESULTS: Midwives identified three intersecting and predisposing root causes: (1) systemic racism; (2) the epigenetic legacy of enslavement; and (3) ongoing cultural loss. In response to these stressors, midwives recommended variants of two additional themes-(4) community building; and (5) culturally centered care-as essential to reversing mortality trends among Black babies. DISCUSSION: Midwives' perspectives, which are supported by relevant literature, provide critical insights that should inform both research and policy aimed at promoting birth justice in the United States and beyond.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".