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Record W3174077803 · doi:10.24095/hpcdp.41.6.04

Arthritis liaison: a First Nations community-based patient care facilitator

2021· article· en· W3174077803 on OpenAlexafffundvenueabout
Valerie Umaefulam, Adalberto Loyola‐Sánchez, Valene Bear Chief, Ana Rame, Louise Crane, Tessa Kleissen, Lynden Crowshoe, Tyler White, Diane Lacaille, Cheryl Barnabé

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2021
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsResearch CanadaUniversity of British ColumbiaUniversity of Calgary
FundersCanadian Institutes of Health ResearchArthritis Society
KeywordsMedicineFacilitatorSpecialtyIntervention (counseling)Health careNursingFamily medicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: Arthritis is a leading cause of disability in First Nations communities and is often accompanied by other chronic diseases. Existing care models prioritize accessibility to specialty care for treatment, whereas patient-centred approaches support broader health goals. METHODS: A patient care facilitator model of care, termed "arthritis liaison," was developed with the community to support culturally relevant patient-centred care plans. Following a one-year-long intervention, we report on the feasibility and acceptability of this care model from the perspectives of patients and health care providers. RESULTS: The arthritis liaison served as a bridge between the clinicians and patients, and fostered continuity, helping patients receive coordinated care within the community.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0120.002
Scholarly communication0.0020.001
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0170.002

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 head0.312
Teacher spread0.286 · 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 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

Citations5
Published2021
Admission routes4
Has abstractyes

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