Bibliographic record
Abstract
The Affordable Care Act ("ACA") made health insurance accessible to many. Yet unaffordable insurance still abounds. This Article proposes a strategy for improving affordability that enables health insurance purchasers to choose, within reasonable limits, which treatments their insurance covers. After critiquing recently proposed strategies for improving affordability and reviewing past legal scholarship on content choice in health insurance, this Article introduces the "Affordable Choices" framework. This framework regulates choice in four ways. First, health plans should only exclude treatments whose merits are subject to reasonable disagreement among patients and physicians. Second, plans should appeal to purchasers' health-related values- values about the sort of life they want to live-rather than predictions about their health status. Third, plans should include interventions like vaccines that protect others from harm. Fourth, excluded interventions should be those costly enough that exclusion meaningfully shifts affordability. This Article will then discuss potential plan offerings, such as "international reference coverage" based on national plans in other developed countries like Canada or the United Kingdom, and discuss what legal reforms-if any-would be needed in order to offer Affordable Choices plans as part of ACA exchanges, employer-provided insurance, Medicare, or Medicaid. This Article then considers legal and ethical objections that ill people, plan purchasers, society, or providers might advance. It first addresses the objection that Affordable Choices plans will unfairly raise health insurance costs for less healthy people and argues that requiring plans to appeal to healthrelated values rather than health status expectations will help to avoid this problem. It then explains how the Affordable Choices framework can be structured to protect purchasers from misprediction or choice overload. Turning to objections from society, this Article explains how choice about the content of insurance is compatible with solidarity among insured patients, albeit liberal solidarity (focused on the framework that enables choice for all) rather than communitarian solidarity (focused on the substantive content of individuals' choices). It also explains how Affordable Choices plans can be compatible with state requirements regarding specific benefits and with an- tidiscrimination law. Last, it explains why participation in Affordable Choices plans accords, rather than conflicts, with providers' legal obligations and ethical duties, and argues that providers are not only permitted but ethically encouraged to protect patients from financial hardship.
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.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".