Most Americans Don’t Grasp Basic Health Insurance Concepts
Bibliographic record
Abstract
The Urban Institute’s Health Reform Monitoring Survey (HRMS), initiated in 2013, is a valuable way to obtain early data about the American public’s ability to understand health insurance concepts and the Affordable Care Act (ACA), before federal government survey data are available. A new study, being released today as a Web First by Health Affairs, provides results from the round of the survey that was fielded in the summer of 2013. The survey found that more than 60 percent of the target market for the health insurance exchanges indicated that they did not understand key concepts related to health insurance. Among the population targeted by the exchanges, only 39.9 percent of respondents understood all nine key concepts: premiums, deductibles, copayments, coinsurance, maximum annual out-of-pocket spending limits, provider networks, covered services, annual limits on services, and noncovered or excluded services. Only 23.6 percent of uninsured respondents, and less than a third of those ages 18–30, were confident that they understood these concepts. The HRMS results are based on a nationally representative, probability-based Internet panel that is fielded each quarter.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".