Increased Medicaid Financing and Equalization of African Americans' and Whites' Outpatient and Emergency Treatment Expenditures.
Bibliographic record
Abstract
OBJECTIVE: We investigated whether a new funding opportunity to finance mental health treatment, provided to autonomous county-level mental health systems without customary cost sharing requirements, equalized African American and White children's outpatient and emergency treatment expenditure inequalities. Using Whites as a benchmark, we considered expenditure patterns favoring Whites over African Americans ("disparities") and favoring African Americans over Whites ("reverse disparities"). METHODS: Settlement-mandated Early Periodic Screening Diagnosis and Treatment (EPSDT) expenditure increases began in the third quarter of 1995. We analyzed Medi-Cal paid claims for mental health services delivered to youth (under 18 years of age) over 64 quarters for a study period covering July 1, 1991 through June 30, 2007 in controlled cross-sectional (systems), longitudinal (quarters) analyses. RESULTS: Settlement-mandated increases in EPSDT treatment funding was associated with relatively greater African American vs. White expenditures for outpatient care when systems initially spent more on Whites. When systems initially spent more on African Americans, relative increases were greater for Whites for outpatient and emergency services. CONCLUSIONS: With new funding that requires no matching funds from the county, county mental health systems did reduce outpatient treatment expenditure inequalities. This was found to be true in counties that initially favored African Americans and in counties that initially favored Whites. Adopting a systems level perspective and taking account of initial conditions and trends can be critical for understanding inequalities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".