Bibliographic record
Abstract
In health care markets, moral hazard is conventionally viewed as a demand‐side phenomenon in which insurance causes patients to use more care because it reduces the price they have to pay for care. However, demand‐side moral hazard cannot explain why U.S. per capita health care costs are much higher than those of countries with universal coverage and lower out‐of‐pocket charges. Instead, blame rests with a phenomenon that may be called supply‐side moral hazard, which occurs when third‐party payment removes the constraints the demand curve would otherwise exert over the prices providers charge, and the quantity of expensive services they can sell. Public institutions are better positioned than private entities to address supply‐side moral hazard. This helps explain why the other wealthy democracies—both those with single‐payer systems, like Canada, and those with multipayer systems and all‐payer procedures for setting provider rates, like Germany and Switzerland—spend much less per capita than the United States. Although managed care achieved some success in controlling U.S. provider prices in the 1990s, in the longer term, it motivated increasing market concentration among providers, which vitiated cost control. Furthermore, managed care exacerbates inequity and complexity, problems that public price regulation avoids.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".