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Record W4249012962 · doi:10.1201/ebk1439829554-2

The Current U.S. Healthcare “Mess”

2010· book-chapter· en· W4249012962 on OpenAlexaboutno aff
William Roth

Bibliographic record

VenueProductivity Press eBooks · 2010
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careCurrent (fluid)BusinessPolitical scienceEngineeringElectrical engineeringLaw

Abstract

fetched live from OpenAlex

Symptoms of the “illness” in people&s;s healthcare system are easy to uncover. The breakdown between government funds (tax) and private funds spent on healthcare in countries with a universal healthcare system (UHS) is approximately 70 percent to 30 percent , respectively. In this scenario, the cost of private insurance is definitely held down by competition, but not so much by competition between the individual private insurance firms. People eligible for Medicare end up spending approximately 20 percent of their income on healthcare, making up the difference between what Medicare pays and the actual cost. Individual states decide the level of income necessary for eligibility. States also have been given responsibility for administering Medicaid so that there are fifty different programs in the country at this point with little integration of efforts. In countries with UHS, one bureaucracy frequently handles all the administrative matters for the entire country, or for each province, as in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.108
GPT teacher head0.286
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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