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Record W3015188131 · doi:10.1377/hlthaff.2019.00826

Innovative Integrated Health And Social Care Programs In Eleven High-Income Countries

2020· article· en· W3015188131 on OpenAlexaff
Onil Bhattacharyya, James Shaw, Samir K. Sinha, Dara Gordon, Simone Shahid, Walter P. Wodchis, Geoffrey M. Anderson

Bibliographic record

VenueHealth Affairs · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsMount Sinai HospitalWomen's College Hospital
Fundersnot available
KeywordsHealth careBusinessContext (archaeology)NursingPopulationSocial determinants of healthEconomic growthPublic relationsMedicinePolitical scienceEnvironmental healthEconomicsGeography

Abstract

fetched live from OpenAlex

High-income countries face the challenge of providing effective and efficient care to the relatively small proportion of their populations with high health and social care needs. Recent reports suggest that integrated health and social care programs target specific high-needs population segments, coordinate health and social care services to meet their clients' needs, and engage clients and their caregivers. We identified thirty health and social care programs in eleven high-income countries that delivered care in new ways. We used a structured survey to characterize the strategies and activities used by these programs to identify and recruit clients, coordinate care, and engage clients and caregivers. We found that there were some common features in the implementation of these innovations across the eleven countries and some variation related to local context or the clients served by these programs. Researchers could use this structured approach to better characterize the core components of innovative integrated care programs. Policy makers could use this approach to provide a common language for international policy exchange, and this structured characterization of successful programs could play an important role in spreading them and scaling them up.

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 score0.538
Threshold uncertainty score0.745

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 head0.046
GPT teacher head0.334
Teacher spread0.288 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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