MétaCan
Menu
Back to cohort
Record W2772974653 · doi:10.1377/hlthaff.2017.1299

National Health Care Spending In 2016: Spending And Enrollment Growth Slow After Initial Coverage Expansions

2017· article· en· W2772974653 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 · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsPer capitaHealth spendingMedicaidHealth careGross domestic productMedical prescriptionSlowdownPrescription drugDemographic economicsConsumer spendingHealth insuranceEconomicsBusinessMedicineEconomic growthEnvironmental healthPopulationRecession

Abstract

fetched live from OpenAlex

Total nominal US health care spending increased 4.3 percent and reached $3.3 trillion in 2016. Per capita spending on health care increased by $354, reaching $10,348. The share of gross domestic product devoted to health care spending was 17.9 percent in 2016, up from 17.7 percent in 2015. Health spending growth decelerated in 2016 following faster growth in 2014 and 2015 associated with coverage expansions under the Affordable Care Act (ACA) and strong retail prescription drug spending growth. In 2016 the slowdown was broadly based, as spending for the largest categories by payer and by service decelerated. Enrollment trends drove the slowdown in Medicaid and private health insurance spending growth in 2016, while slower per enrollee spending growth influenced Medicare spending. Furthermore, spending for retail prescription drugs slowed, partly as a result of lower spending for drugs used to treat hepatitis C, while slower use and intensity of services drove the slowdown in hospital care and physician and clinical services.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.061
GPT teacher head0.335
Teacher spread0.274 · 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