Achieving Universal Health Care in the United States Using International Models
Bibliographic record
Abstract
Achieving Universal Health Care in the United States Using International Models by Jessica A. Hohman Despite its reputation as a leader in groundbreaking biomedical technology and innovative life-extending procedures, the United States today finds itself plagued by a national health care system in dire need of reform. With the number of uninsured Americans burgeoning to over 45 million, policymakers are struggling to ensure wide access, low costs, and first-rate care. With its high level of health care expenditures, the U.S. remains one of the few industrialized states without a universal health care system. The first half of this thesis examines the financing and delivery mechanisms in the health systems of Canada, the Netherlands, France, Germany, and the United Kingdom— with a focus on how they have achieved universal health coverage. The second half of this thesis applies these models in an analysis of how the U.S. can achieve universal health care—with an emphasis on the state-led federalist approach over single-payer, population-based expansions, tax credits, and employer and individual mandate reform pathways.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".