The Changing Character of the Black–White Infant Mortality Gap, 1983–2004
Bibliographic record
Abstract
OBJECTIVES: We examined how changes in demographic, geographic, and childbearing risk factors were related to changes in the Black-White infant mortality rate (IMR) gap over 2 decades. METHODS: Using 1983-2004 Vital Statistics, we applied inverse probability weighting methods to examine the relationship between risk factors and 3 outcomes: the overall IMR gap, its birth weight component, and its conditional (on birth weight) IMR component. RESULTS: The unexplained IMR gap (the part not related to observed risk factors) was stable, changing from 5.0 to 5.3 deaths per 1000 live births. By contrast, the explained gap declined from 4.6 to 1.9. The decline in the explained gap was driven by the changing relationship between risk factors and IMR. Further analysis revealed that most of the unexplained gap occurred among infants weighing less than 1000 grams at birth, whereas most of the explained gap occurred among infants weighing more than 1000 grams. CONCLUSIONS: The unexplained gap was stable over the last 2 decades, but the explained gap declined markedly. If the stability of the unexplained gap continues, even complete convergence of risk factors would reduce the Black-White IMR gap by only one quarter.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".