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Record W2227067052 · doi:10.12927/hcq.2015.24433

A Patient-Centred Redesign Framework to Support System-Level Process Changes for Multimorbidities and Chronic Conditions

2015· article· en· W2227067052 on OpenAlexaff
Tara Sampalli, Lynn Edwards, Erin Christian, Graeme Kohler, Lisa Bedford, Jillian Demmons, Jennifer Verma, Rick Gibson, Shannon Ryan Carson

Bibliographic record

VenueHealthcare Quarterly · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsCanadian Foundation for Healthcare ImprovementNova Scotia Health Authority
Fundersnot available
KeywordsProcess (computing)Process managementFoundation (evidence)Patient careChronic careMultiple Chronic ConditionsMedicineComputer scienceChronic diseaseBusinessNursingIntensive care medicinePolitical science

Abstract

fetched live from OpenAlex

Recent trends show an increase in the prevalence and costs associated with managing individuals with multimorbidities. Enabling better care for these individuals requires system-level changes such as the shift from a focus on a single disease or single service to multimorbidities and integrated systems of care. In this paper, a novel patient-centred redesign framework that was developed to support system-level process changes in four service areas has been discussed. The novelty of this framework is that it is embedded in patient perspectives and in the chronic care model as the theoretical foundation. The aims of this paper are to present an application of the framework in the context of four chronic disease prevention and management services, and to discuss early results from the pilot initiative along with an overview of the spread opportunities for this initiative.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0070.006
Open science0.0040.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.128
GPT teacher head0.380
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations9
Published2015
Admission routes1
Has abstractyes

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