We All Want It, but We Don't Know What It Is: Toward a Standard of Affordability for Health Insurance Premiums
Bibliographic record
Abstract
The 2010 Patient Protection and Affordable Care Act (P.L. 111-148), or ACA, requires that U.S. citizens either purchase health insurance or pay a fine. To offset the financial burden for lower-income households, it also provides subsidies to ensure that health insurance premiums are affordable. However, relatively little work has been done on how such affordability standards should be set. The existing literature on affordability is not grounded in social norms and has methodological and theoretical flaws. To address these issues, we developed a series of hypothetical vignettes in which individual and household sociodemographic characteristics were varied. We then convened a panel of eighteen experts with extensive experience in affordability standards to evaluate the extent to which each vignette character could afford to pay for one of two health insurance plans. The panel varied with respect to political ideology and discipline. We find that there was considerable disagreement about how affordability is defined. There was also disagreement about what might be included in an affordability standard, with substantive debate surrounding whether savings, debt, education, or single parenthood is relevant. There was also substantial variation in experts' assessed affordability scores. Nevertheless, median expert affordability assessments were not far from those of ACA.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".