Does level of minority presence and hospital reimbursement policy influence hospital referral region health rankings in the United States
Bibliographic record
Abstract
The shift from a fee-for-service payment to a value-based payment scheme, sparked by the Patient Protection and Affordable Care Act, introduced pay-for-performance programs such Hospital Value Based Purchasing. Previous inquiry has not considered how local community factors may affect hospital system performance. This study investigated the association between local health performance and minority population in a hospital referral region (HRR). The primary objective was to ascertain whether community diversity levels are significantly associated to local health performance guided by the ecological model. Secondary data analysis collected from the 2016 American Hospital Association, Area Health Resource File, Commonwealth Fund Scorecard on Local Health System Performance, and the Dartmouth Atlas HRR dataset was used. Our primary findings show that the more diverse a HRR is, the more likely it is to be associated with lower ranking for access and affordability prevention and treatment avoidable hospital use and cost as well as healthy lives. Total performance score was significantly related to a better health ranking on prevention and treatment, hospital use, and cost, as well as healthy lives. This research supports the assertion that communities, particularly minorities in those communities, affect local health care performance in a variety of ways.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".