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Record W2147251283 · doi:10.1215/03616878-2854919

Oregon's Experiment in Health Care Delivery and Payment Reform: Coordinated Care Organizations Replacing Managed Care

2014· article· en· W2147251283 on OpenAlexaff
Steven W. Howard, Stephanie Bernell, Jangho Yoon, Jeff Luck, C. M. Ranit

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsNorth Pacific Marine Science Organization
Fundersnot available
KeywordsMedicaidAccountabilityPaymentTransparency (behavior)BusinessHealth careCorporate governanceDelivery systemQuality (philosophy)Health care deliveryNursingMedicinePolitical scienceFinanceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

To control Medicaid costs, improve quality, and drive community engagement, the Oregon Health Authority introduced a new system of coordinated care organizations (CCOs). While CCOs resemble traditional Medicaid managed care, they have differences that have been deliberately designed to improve care coordination, increase accountability, and incorporate greater community governance. Reforms include global budgets integrating medical, behavioral, and oral health care and public health functions; risk-adjusted payments rewarding outcomes and evidence-based practice; increased transparency; and greater community engagement. The CCO model faces several implementation challenges. If successful, it will provide improved health care delivery, better health outcomes, and overall savings.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0100.006
Open science0.0010.004
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.400
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2014
Admission routes1
Has abstractyes

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