Bibliographic record
Abstract
The United States is one of the largest advanced economies by gross domestic product (GDP) in terms of both nominal and purchasing power parity. Especially in healthcare, the U.S. is the leading power in state-of-art medical technology, training, and research. However, healthcare spending in the U.S is remarkably highest with scanty health outcomes and poor public service compared with the ten highest-income countries (United Kingdom, Canada, Germany, Australia, Japan, Sweden, France, the Netherlands, Switzerland, and Denmark). From 1960 to 2017, U.S. healthcare expenditure as a percentage of GDP inflated from 5.0 to 17.9 (i.e., $3.5 trillion), and average dollars spent on individuals increased from (in dollars) 146 to 10,739 respectively. Among these national healthcare expenditures, nearly 25% of spending was wasted. Early origins of the deliberate steps to change the American healthcare system through a controlled form of financing and healthcare delivery traced back to the Nixon administration during 19 century. The significant structural changes in the U.S. healthcare system started in 1970. Congress passed a bill in 1973, the Health Maintenance Organization Act, which spurred the rapid growth of Health Maintenance Organizations (HMOs), the first form of managed care. Under traditional insurance (also known as fee-for-service or indemnity insurance), insurance companies and providers operated independently without incentive, resulting in unaffordability and unrestrained delivery of services with spiraling health insurance premiums. This integration of financing and insurance was an efficient way to gain control and prompted the explosion of managed care during the 1970s and 1980s, lifting a heavy burden from employers.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".