Multimethod Evaluation of Health Policy Change: An Application to Medicaid Managed Care in a Rural State
Bibliographic record
Abstract
OBJECTIVE: To answer questions about the impacts of Medicaid managed care (MMC) at the individual, organizational/community, and population levels of analysis. DATA SOURCES/STUDY SETTING: Multimethod approach to study MMC in New Mexico, a rural state with challenging access barriers. STUDY DESIGN: Individual level: surveys to assess barriers to care, access, utilization, and satisfaction. Organizational/community level: ethnography to determine changes experienced by safety net institutions and local communities. Population level: analysis of secondary databases to examine trends in preventable adverse sentinel events. SURVEY: multivariate statistical methods, including factor analysis and logistic regression. Ethnography: iterative coding and triangulation to assess documents, field observations, and in-depth interviews. Secondary databases: plots of sentinel events over time. PRINCIPAL FINDINGS: The survey component revealed no consistent changes after MMC, relatively favorable experiences for Medicaid patients, and persisting access barriers for the uninsured. In the ethnographic component, safety net institutions experienced increased workload and financial stress; mental health services declined sharply. Immunization rate, as an important sentinel event, deteriorated. CONCLUSIONS: MMC exerted greater effects on safety net providers than on individuals and did not address problems of the uninsured. A multimethod approach can facilitate evaluation of change in health policy.
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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 | 0.076 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".