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The reimagination of sustainable integrated care in Ontario, Canada

2020· article· en· W3097318267 on OpenAlexafffundabout
Gayathri Embuldeniya, Jennifer Gutberg, Walter P. Wodchis

Bibliographic record

VenueHealth Policy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsTrillium Health CentreUniversity of Toronto
FundersOntario Ministry of Health and Long-Term Care
KeywordsConceptualizationAccountabilityIntegrated careSustainabilityControl (management)Work (physics)NursingPublic relationsPsychologyPolitical scienceMedicineBusinessHealth careManagementEngineeringEconomicsComputer science

Abstract

fetched live from OpenAlex

PURPOSE/SETTING: To encourage clinical and financial efficiency, the Canadian province of Ontario initiated an integrated care program - Integrated Funding Models (IFMs) that required collaboration and coordination across acute and post-acute care sectors. This research shows how program implementers went beyond policy-makers' original designs, to make integrated care sustainable for chronic diseases. METHODS: Forty-five interviews were conducted with program participants at three chronic disease programs, as well as with policymakers. Interviews were conducted over two phases; during early implementation in 2016, and as programs matured in 2018. Data were analyzed through a cultural constructivist lens to understand how participants shaped programs. FINDINGS: Participants desired greater accountability and control. Participants in the first program wanted localized control over decision-making. In the second, participants initiated greater control over financial uncertainty. In the third program, hospital participants sought greater control over community care. Participants across programs simultaneously wanted integrated care to be expanded holistically, spatially, and temporally for patients, extending the length of care, and expanding the spaces in which care was provided. Findings also suggest a gap between program implementers' and policymakers' conceptualizations of integrated care. CONCLUSION: This work shows how IFMs were reimagined in ways that transcended their original conceptualization as spatially and temporally delimited initiatives aimed at improving coordination and efficiency. It has practical implications for those facing sustainability challenges in other contexts.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.437
Teacher spread0.412 · 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 designNot applicable
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

Citations18
Published2020
Admission routes3
Has abstractyes

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