The effectiveness of providing evidenced-based perinatal practice to low-income populations providing perinatal care: Does patient income influence the delivery of quality care?
Bibliographic record
Abstract
Background: This study examines a national perinatal quality improvement collaborative designed to create high reliability through the use of evidence-based perinatal care bundles. The objective of this study is to determine whether hospitals serving low-income patient populations experienced lower compliance with perinatal care bundles than hospitals serving higher-income patient populations Objective: We investigated the relationship between the rate of perinatal bundle compliance within a hospital and the economic characteristics of the patients and surrounding community. We hypothesized a negative relationship between poverty and care bundle compliance. Methods: Using prospective data from 131,847 births over 34 months within 16 hospitals located in cities across the United States, we examined the relationship between compliance with evidence-based obstetrical care bundles and three measures of the poverty status of the patient population served and the hospital service area: 1) proportion of the obstetrical patients with Medicaid as the primary payer, 2) median income in the hospital service area, and 3) poverty rate in the hospital’s service area. Results: The findings indicate no difference in bundle compliance rates in relation to the economic characteristics of the participating hospitals and their patients. Conclusions: While previous research has indicated that patients of lower socioeconomic status are less likely to receive high quality care, the findings in this study indicate that hospital compliance with evidence-based perinatal care bundles did not differ by economic characteristics of the hospital service area. These results indicate uniformity of care across hospitals irrespective of patient economic characteristics.
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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 | 0.024 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".