MétaCan
Menu
Back to cohort
Record W2165799442 · doi:10.2190/pjby-et6g-bmqk-4exv

The Number of Americans without Health Insurance Rose in 2001 and Continued to Rise in 2002

2003· article· en· W2165799442 on OpenAlexaboutno aff
Leighton Ku

Bibliographic record

VenueInternational Journal of Health Services · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicaidQuarter (Canadian coin)UnemploymentHealth insuranceBusinessDemographic economicsCensusMicrodata (statistics)Economic shortageEconomic growthHealth carePopulationEconomicsEnvironmental healthGovernment (linguistics)MedicineGeography

Abstract

fetched live from OpenAlex

Data from the Census Bureau and the Centers for Disease Control and Prevention indicate that the number of uninsured Americans rose in 2001and in the first quarter of 2002. The main reason insurance coverage fell was a drop-off in employer-sponsored insurance for workers and their dependents. This reduction was triggered by rising unemployment levels and rising health insurance premiums, which made it more difficult for employers to offer insurance or for workers to afford it. The downturn in private coverage was partially offset by increased enrollment in Medicaid and the State Children's Health Insurance Program (SCHIP). About two million mor e children and one million more adults would have been uninsured had it not been for the growth in these programs. Funding for the public programs is, however, threatened by budget shortfalls affecting most states, which administer these programs. Many states have cut their Medicaid programs and are planning further cutbacks. Increasing federal assistance to states and their Medicaid programs could help protect insurance coverage for low-income people during the current economic slowdown. Future SCHIP enrollment could drop sharply because of a shortage of federal funds, and Congress could take steps to bolster SCHIP funding.

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)0.002
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 score0.146
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.349
Teacher spread0.312 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Health ServicesSame topicHealthcare Policy and ManagementFrench-language works237,207