MétaCan
Menu
← Back to cohort
Record W4231347631 · doi:10.24124/2018/58764

An eagle's view: perspectives about diabetes in Yekooche First Nation, a first nation's community in northern British Columbia

2018· dissertation· en· W4231347631 on OpenAlexaffabout
Bianca Michell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsDiabetes mellitusPerceptionQualitative researchMedicineGerontologyPsychologySociologySocial science

Abstract

fetched live from OpenAlex

A substantial amount of quantitative literature documents the prevalence and incidents rates and causes of diabetes among First Nations, but few qualitative literature documents exist exploring First Nations perceptions about diabetes and its causes. This study utilized the Two-Eyed Seeing framework to explore Yekooche member’s perceptions about diabetes. The research objectives of the research were: 1) to understand how Yekooche members define diabetes; 2) to explore Yekooche perceptions of diabetes; and, 3) to examine their beliefs and understanding of diabetes education and diabetes educational materials. This qualitative research addressed these objectives through interviews conducted with Yekooche First Nation. Analysis of the interviews generated findings which are presented in story form, providing insights into how participants understand the causes about diabetes, their experience with diabetes related complications, their fears and concerns of children developing diabetes, and the unexpected finding of their experiences with the effects of clear-cutting on their health.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.260
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0280.007
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.396
Teacher spread0.340 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

Explore more

Same topicObesity and Health Practices→French-language works237,207→