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Record W3175140486 · doi:10.1096/fasebj.20.4.a129-c

Baseline Assessment for a Multi‐institutional Diabetes Prevention Program for First Nations

2006· article· en· W3175140486 on OpenAlexafffundabout
Lara S. Ho, Joel Gittelsohn, Amanda Rosecrans, Sangita Sharma, Elizabeth Ford, Stewart B. Harris

Bibliographic record

VenueThe FASEB Journal · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchAmerican Diabetes Association
KeywordsPsychosocialBaseline (sea)Diabetes mellitusMedicineEnvironmental healthFood habitsPhysical activityFood frequency questionnaireGerontologyDemographyPhysical therapyPolitical science

Abstract

fetched live from OpenAlex

Diabetes has reached epidemic proportions in First Nations but there is limited information on physical activity, food and psychosocial risk factors, especially in remote communities. A baseline survey of main food preparers and shoppers (n=149 randomly selected household, remote n=76, semi‐remote n=73) on seven reserves was conducted to assess physical activity and food behaviors and related psychosocial factors using multi‐question scales. Respondents on remote reserves were more likely to have less education (>12 years: 39.47% vs. 62.96%, p=0.008) and come from larger households (mean household members: 4.28 vs. 3.77, p=0.107). Related to this, respondents on remote reserves had lower physical activity and food knowledge scores (2.9 vs. 3.6, p=0.022) and lower outcome expectations for related behaviors (4.04 vs. 4.33, p=0.008). These baseline findings are being used to evaluate a multi‐institutional trial in the communities to prevent diabetes. This work was supported by the American Diabetes Association and the Canadian Institutes of Health Research.

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)0.003
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.031
GPT teacher head0.369
Teacher spread0.338 · 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

Citations1
Published2006
Admission routes3
Has abstractyes

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