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Record W2114493939 · doi:10.1002/ajhb.22257

Overweight and obesity among North American Indian infants, children, and youth

2012· article· en· W2114493939 on OpenAlexaboutno aff
Lawrence M. Schell, Mia V. Gallo

Bibliographic record

VenueAmerican Journal of Human Biology · 2012
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of Environmental Health SciencesU.S. Public Health Service
KeywordsOverweightObesityBody mass indexPsychosocialEthnic groupDemographyMedicinePsychological interventionHumPovertyGerontologyChildhood obesityEnvironmental healthPsychiatryPolitical scienceEndocrinologyHistory

Abstract

fetched live from OpenAlex

The frequency of overweight and obesity among North American Indian children and youth exceeds that of other ethnic groups in the United States. This observation is based on studies using body mass index as the primary measure of overweight and obesity. In the mid-20th century, there were regional differences among North American Indian groups in sub-adults' size and shape and only a few Southwestern groups were characterized by high rates of overweight and obesity. In most populations, the high prevalence of overweight and obesity developed in the last decades of the 20th century. Childhood obesity may begin early in life as many studies report higher birth weights and greater weight-for-height in the preschool years. Contributing factors include higher maternal weights, a nutritional transition from locally caught or raised foods to store bought items, psychosocial stress associated with threats to cultural identity and national sovereignty, and exposure to obesogenic pollutants, all associated to some degree with poverty. Obesity is part of the profile of poor health among Native Americans in the US and Canada, and contributes to woefully high rates of diabetes, cardiovascular disease, and early mortality. Interventions that are culturally appropriate are needed to reduce weights at all points in the lifespan.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.256
Teacher spread0.247 · 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.

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

Citations85
Published2012
Admission routes1
Has abstractyes

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