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Record W2508185741 · doi:10.15256/joc.2016.6.83

Meeting the Needs of a Complex Population: A Functional Health- and Patient-Centered Approach to Managing Multimorbidity

2016· article· en· W2508185741 on OpenAlexaff
Tara Sampalli, Robert C. Dickson, Jill A. Hayden, Lynn Edwards, Arun Salunkhe

Bibliographic record

VenueJournal of Comorbidity · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMultimorbidityMedicineHealth careService delivery frameworkNursingInclusion (mineral)Chronic careIntegrated careService (business)PopulationProcess managementFamily medicinePsychologyChronic diseaseBusinessEnvironmental health

Abstract

fetched live from OpenAlex

Individuals with multimorbidity have complex care needs along with significant impacts to their functional health and quality of life. Recent evidence-based and experience-based explorations have revealed the importance of patient perspectives and functional health management in improving care delivery and health outcomes for individuals with multimorbidity. The impact of managing multimorbidity is evident at multiple levels of healthcare - the individual, the provider, and the system. Our local experience dealing with these challenges has led to the development of a functional health model that includes patient perspectives in care delivery within the Integrated Chronic Care Service (ICCS) of the health authority in Nova Scotia. In this paper, we present a discussion of the challenges, guiding models, and service-level transformations that have been integrated into care delivery at the ICCS to meet the healthcare needs of people with multiple health conditions. We describe our redesign strategies for care team planning, treatment approach, and patient inclusion.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.107
GPT teacher head0.324
Teacher spread0.217 · 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 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

Citations11
Published2016
Admission routes1
Has abstractyes

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