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Record W3097030946 · doi:10.5334/ijic.5520

Goal-Oriented Care: A Catalyst for Person-Centred System Integration

2020· article· en· W3097030946 on OpenAlexaffabout
Carolyn Steele Gray, Agnes Grudniewicz, Alana Armas, James W. Mold, Jennifer Im, Pauline Boeckxstaens

Bibliographic record

VenueInternational Journal of Integrated Care · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of OttawaUniversity of TorontoLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsIntegrated careHealth careFormative assessmentProcess managementNursingNormativeKnowledge managementPsychologyBusinessMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: Person-centred integrated care is often at odds with how current health care systems are structured, resulting in slower than expected uptake of the model worldwide. Adopting goal-oriented care, an approach which uses patient priorities, or goals, to drive what kinds of care are appropriate and how care is delivered, may offer a way to improve implementation. DESCRIPTION: This case report presents three international cases of community-based primary health care models in Ottawa (Canada), Vermont (USA) and Flanders (Belgium) that adopted goal-oriented care to stimulate clinical, professional, organizational and system integration. The Rainbow Model of Integrated Care is used to demonstrate how goal-oriented care drove integration at all levels. DISCUSSION: The three cases demonstrate how goal-oriented care has the potential to catalyse integrated care. Exploration of these cases suggests that goal-oriented care can serve to activate formative and normative integration mechanisms; supporting processes that enable integrated care, while providing a framework for a shared philosophy of care. LESSONS LEARNED: By establishing a common vision and philosophy to drive shared processes, goal-oriented care can be a powerful tool to enable integrated care delivery. Offering plenty of opportunities for training in goal-oriented care within and across teams is essential to support this shift.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.375
Teacher spread0.345 · 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

Citations60
Published2020
Admission routes2
Has abstractyes

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