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The diabetes experiences of Aboriginal people living in a rural Canadian community

2005· article· en· W2112506566 on OpenAlexafffundabout
Sylvia S. Barton, Nancy Anderson, Harvey V. Thommasen

Bibliographic record

VenueAustralian Journal of Rural Health · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British ColumbiaUniversity of Northern British Columbia
FundersUniversity of British Columbia
KeywordsQualitative researchGerontologyRelevance (law)MedicineDiabetes mellitusPsychologyNursingSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

OBJECTIVE: To optimise participation with Aboriginal people by sharing experiences of living with the challenges of diabetes in rural south-western Canada, and how these could be addressed. DESIGN: Qualitative content analysis of semi-structured and conversational interviews. SETTING: Diabetes health services in the Bella Coola Valley, British Columbia, Canada. SUBJECTS: Eight Nuxalk Nation participants, five women and three men, living with type 2 diabetes, were interviewed. Four of these participants, three women and one man, were engaged in six follow-up conversational interviews. MAIN OUTCOME MEASURES: The descriptive research explored experiences of Nuxalk people living with the challenges of diabetes, and how these could inform diabetes health services in culturally specific ways. RESULTS: Challenges included understanding the connections between (i) diabetes and western or traditional medicines; (ii) dietary changes, exercise and weight loss; (iii) how health professionals communicate and the relevance of what is said; (iv) having many life choices and the responsibility to choose; and (v) a belief in living day by day and an awareness of life cycles that may need to be broken. CONCLUSION: The study substantiated the fundamental necessity for diabetes health services to be inclusive of Aboriginal perspectives.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
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 head0.018
GPT teacher head0.347
Teacher spread0.328 · 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.

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

Citations42
Published2005
Admission routes3
Has abstractyes

Explore more

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