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Record W2986783720 · doi:10.5334/ijic.4648

Patient Navigation as an Approach to Improve the Integration of Care: The Case of NaviCare/SoinsNavi

2019· article· en· W2986783720 on OpenAlexaff
Shelley Doucet, Alison Luke, Jennifer Splane, Rima Azar

Bibliographic record

VenueInternational Journal of Integrated Care · 2019
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMount Allison University
Fundersnot available
KeywordsIntegrated careHealth careService (business)NursingProcess (computing)Work (physics)PopulationKnowledge managementMedicinePsychologyBusinessComputer scienceEngineeringMarketingPolitical science

Abstract

fetched live from OpenAlex

Children and youth with complex care needs require more and varied healthcare services than the average population, as well as a high degree of coordinated care. Evidence has shown that these individuals and their families have better outcomes if they have access to integrated care. Patient navigation can serve as a novel approach to improve the integration of care for individuals with complex care needs in an increasingly fragmented system. NaviCare/SoinsNavi is an example of a navigation centre for children and youth with complex care needs, their families, and the care team. This research-based service is aimed at facilitating more convenient and integrated care using a personalized family-centred approach. NaviCare/SoinsNavi employs two patient navigators who work with clients to formulate and prioritize goals based on their unmet needs. The centre serves as a living laboratory, which provides researchers, knowledge users, and clients a real life setting where innovative ideas can be explored, evaluated, modified as needed throughout the research process, and moved into policy in an efficient manner. Patient navigation programs can contribute to decreasing fragmentation, improving access, and promoting integrated care across disciplines, settings, and sectors for individuals across the lifespan.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.016
GPT teacher head0.400
Teacher spread0.384 · 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

Citations36
Published2019
Admission routes1
Has abstractyes

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