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Record W1985648932 · doi:10.12927/hcq.0000.22580

Integrated Complex Care Model: Lessons Learned from Inter-organizational Partnership

2011· article· en· W1985648932 on OpenAlexafffundabout
Eyal Cohen‬‏, Cindy Bruce-Barrett, Shauna Kingsnorth, Krista Keilty, Anna Mary Cooper-Ryan, Stacey Daub

Bibliographic record

VenueHealthcare Quarterly · 2011
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsHome and Community Care Support ServicesHolland Bloorview Kids Rehabilitation HospitalHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsOperationalizationGeneral partnershipHealth careIntegrated careBusinessPublic relationsNursingKnowledge managementProcess managementMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Providing integrated care for children with medical complexity in Canada is challenging as these children are, by definition, in need of coordinated care from disparate providers, organizations and funders across the continuum in order to optimize health outcomes. We describe the development of an inter-organizational team constructed as a unique tripartite partnership of an acute care hospital, a children's rehabilitation hospital and a home/community health organization focused on children who frequently use services across these three organizations. Model building and operationalization within the Canadian healthcare system is emphasized. Key challenges identified to date include communication and policy barriers as well as optimizing interactions with families; critical enablers have been alignment with policy trends in healthcare and inter-organizational commitment to integrate at the point of care. Considerations for policy developments supporting full integration across service sectors are raised. Early indicators of success include the enrolment of 34 clients and patients and the securing of funds to evaluate and expand the model to serve more children.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.274
GPT teacher head0.430
Teacher spread0.156 · 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; both teacher heads agree on what is shown here.

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

Citations26
Published2011
Admission routes3
Has abstractyes

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