Access to Care, Provider Choice and Racial Disparities
Bibliographic record
Abstract
This paper explores whether choice of provider explains any of the observed infant health gradients, and if so, why poor women choose different providers than their richer neighbors.We exploit an exogenous change in policy that occurred in California in the early 1990s that suddenly increased Medicaid payments to hospitals and which lead to a sharp change in where women with Medicaid delivered.To characterize the extent to which poor women responded to the increase in provider access, we calculate hospital segregation indices (which measure the extent to which Medicaid mothers delivered in separate hospitals than privately insured mothers residing in the same geographic area) both before and after the policy change for each market in California and show that it fell sharply after the policy change.Even though black mothers responded least to the increase in provider choice afforded by the policy change, they benefited the most from hospital desegregation in terms of reduced neonatal mortality and decreased incidence of very low birth weight.In contrast, other groups with lower initial neonatal mortality moved more and gained less in terms of improvements in birth 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".