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Record W3118171272 · doi:10.1093/cdn/nzaa175

Understanding Barriers to Implementing and Managing Therapeutic Diets for People Living with Chronic Kidney Disease in Remote Indigenous Communities

2020· article· en· W3118171272 on OpenAlexafffundabout
Rebecca Schiff, Holly Freill, Crystal N Hardy

Bibliographic record

VenueCurrent Developments in Nutrition · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsThunder Bay Regional Health Sciences CentreLakehead University
FundersNorthern Ontario Academic Medicine Association
KeywordsIndigenousKidney diseaseContext (archaeology)MedicineDiseaseGerontologyEnvironmental healthGeographyPathologyEcologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Indigenous peoples in Canada, and globally, experience a disproportionate burden of chronic kidney disease (CKD) and end-stage renal disease (ESRD) ESRD patients in remote Indigenous communities might experience significant challenges in adhering to dietary guidelines. Much research has documented the poor quality, high cost, and limited availability of healthy foods in remote, Indigenous communities. Food quality and availability are poor in remote communities, indicating that persons with ESRD and CKD might have limited ability to adhere to dietary guidelines. This article reports on research designed to understand food-access barriers in remote First Nations for persons living with stage 4 and 5 CKD/ESRD. The study involved semi-structured interviews with 38 patients in remote communities. It concludes with some reflections on the significance of this issue in the context of dietetic practice.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.301
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.255
GPT teacher head0.429
Teacher spread0.174 · 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 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

Citations4
Published2020
Admission routes3
Has abstractyes

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