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Record W2613004394 · doi:10.3390/children4050038

First Nations Approaches to Childhood Obesity: Healthy Lifestyles in Canada Compared with Alternatives for Alaska Native Communities

2017· article· en· W2613004394 on OpenAlexaboutno aff
Peter de Schweinitz, Janet M. Wojcicki

Bibliographic record

VenueChildren · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsObesityGovernment (linguistics)SubsidyGeographyChildhood obesityNative americanCommunity healthGerontologyEnvironmental healthEconomic growthSocioeconomicsMedicinePolitical scienceHealth careSociologyEthnologyEconomics

Abstract

fetched live from OpenAlex

Alaska Native and American Indian children have among the highest prevalence of obesity in the United States. Canadian Aboriginal populations including First Nations also have high rates of obesity but obesity rates among children are noticeably lower. We highlight some of the important differences between American and Canadian approaches to healthy lifestyles and Aboriginal/Native health, including diet and physical activity, which may in part explain the differences in obesity prevalence. Specifically, the Canadian government provides a food subsidy program to bring perishable fruits and vegetable to remote, rural Canadian areas and secondly supports the use of traditional foods and harvesting/gathering through a number of government supported programs. Lastly, there may be a better sense of community and overall life satisfaction for Aboriginals compared with Alaska Natives, in part because of the incorporation of healthcare and other services within the larger overall community, as opposed to separate services as is the case for Alaska Natives. This perspective provides insight into some of these potential differences.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0150.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.102
GPT teacher head0.328
Teacher spread0.226 · 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 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

Citations7
Published2017
Admission routes1
Has abstractyes

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