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Record W3014707438 · doi:10.1377/hlthaff.2019.01587

Innovative Policy Supports For Integrated Health And Social Care Programs In High-Income Countries

2020· article· en· W3014707438 on OpenAlexaff
Walter P. Wodchis, James C. Shaw, Samir K. Sinha, Onil Bhattacharyya, Simone Shahid, Geoffrey M. Anderson

Bibliographic record

VenueHealth Affairs · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsMount Sinai HospitalWomen's College HospitalInstitute of Health Services and Policy Research
Fundersnot available
KeywordsWorkforceBusinessStaffingAccountabilityHealth careCorporate governanceIntegrated carePaymentEconomic growthFinanceNursingEconomicsMedicinePolitical science

Abstract

fetched live from OpenAlex

As high-income countries face the challenge of providing better and more efficient integrated health and social care to high-needs and high-cost populations, they may require innovative policy supports at both the national and local levels. We categorized policy supports into four areas: governance and partnerships; workforce and staffing; financing and payment; and data sharing and use. Our structured survey of thirty integrated health and social care programs in high-income countries in 2018 found that the majority of programs had policy supports in two or more areas, with supports for governance and partnerships and for workforce and staffing being the most common. Financing and payment and data sharing and use were less common. Local partnerships empowered integration across sectors, and new staff roles that spanned health and social care embedded this integration in care delivery. National policies-including bundled financing and investment in data-enabled integration and cross-sector accountability.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.043
GPT teacher head0.359
Teacher spread0.317 · 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 designQualitative
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

Citations28
Published2020
Admission routes1
Has abstractyes

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