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Record W2992143362 · doi:10.1377/hlthaff.2019.01451

National Health Care Spending In 2018: Growth Driven By Accelerations In Medicare And Private Insurance Spending

2019· article· en· W2992143362 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Affairs · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsHealth spendingHealth careHealth insuranceDemographic economicsBusinessPercentage pointEconomicsEconomic growthFinance

Abstract

fetched live from OpenAlex

US health care spending increased 4.6 percent to reach $3.6 trillion in 2018, a faster growth rate than the rate of 4.2 percent in 2017 but the same rate as in 2016. The share of the economy devoted to health care spending declined to 17.7 percent in 2018, compared to 17.9 percent in 2017. The 0.4-percentage-point acceleration in overall growth in 2018 was driven by faster growth in both private health insurance and Medicare, which were influenced by the reinstatement of the health insurance tax. For personal health care spending (which accounted for 84 percent of national health care spending), growth in 2018 remained unchanged from 2017 at 4.1 percent. The total number of uninsured people increased by 1.0 million for the second year in a row, to reach 30.7 million in 2018.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.303
Teacher spread0.259 · 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