Bibliographic record
Abstract
If single-payer health care is ever to become a reality in the United States, it will very likely be pioneered by a state government, much like Canada’s single-payer system was first adopted in the provinces. Canada’s system operates more like U.S. Medicaid — financed nationally but administered largely by the provinces — than U.S. Medicare. This article describes three basic strategies progressive U.S. state governments are exploring for achieving universal access to high-quality health care and better health outcomes for their residents. First, maximizing eligibility for the existing Medicaid program using matching federal funds. Second, taking up the mantle of Obamacare by adopting state-level replacements for provisions that federal lawmakers repeal, subsidizing and regulating the price of private insurance, and making more affordable coverage available for purchase on state-run health insurance exchanges. Third, I focus particularly on the efforts of states to succeed where federal reformers have failed by adopting a state-level public option or single-payer health care system. Although state-level public-option and single-payer health plans face significant obstacles, they are more feasible than federal reforms. Moreover, I argue, state-level single-payer health care may be preferable from a health justice perspective because it holds greater promise for integrating health care, public health, and social safety net program goals to achieve better health for all. State lawmakers must proceed cautiously, however, particularly with respect to ensuring that people entitled to traditional Medicaid benefits, which offer special coverage for special populations, continue to receive them. Additionally, state lawmakers should carefully assess the role that privatized public coverage currently plays in their health systems and what role, if any, it should play in public-option or single-payer reforms.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".