Multimethod Evaluation of Health Policy Change: An Application to Medicaid Managed Care in a Rural State
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it