Do Medicaid and commercial CAHPS scores correlate within plans?: a New Jersey case study.
Bibliographic record
Abstract
BACKGROUND: The Consumer Assessment of Health Plans Study (CAHPS) health plan survey is currently administered to large independent samples of Medicaid beneficiaries and commercial enrollees for managed care organizations that serve both populations. There is interest in reducing survey administration costs and sample size requirements by sampling these 2 groups together for health plan comparisons. Plan managers may also be interested in understanding variability within plans. OBJECTIVE: The objective of this study was to assess whether the within plan correlation of CAHPS scores for the 2 populations are sufficiently large to warrant inferences about one from the other, reducing the total sample sizes needed. RESEARCH DESIGN: This study consisted of an observational cross-sectional study. SUBJECTS: Subjects were 3939 Medicaid beneficiaries and 3027 commercial enrollees in 6 New Jersey managed care plans serving both populations. MEASURES: Outcomes are 4 global ratings and 6 report composites from the CAHPS 1.0 survey. RESULTS: Medicaid beneficiaries reported poorer care than commercial beneficiaries for 6 composites, but none of the 4 global ratings. Controlling for these main effects, variability between commercial enrollees and Medicaid beneficiaries within plans exceeded variability by plans for commercial enrollees for 4 of the 10 measures (2 composites, 2 global ratings). CONCLUSIONS: Within-plan variability in evaluations of care by Medicaid and commercial health plan member evaluations is too great to permit meaningful inference about plan performance for one population from the other for many important outcomes; separate surveys should still be fielded.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".