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Record W2015548835 · doi:10.5402/2012/504168

Inadequate Nutrient Intakes in Youth of a Remote First NationCommunity: Challenges and the Need for Sustainable Changes inProgram and Policy

2012· article· en· W2015548835 on OpenAlexafffundabout
Allison Gates, Rhona M. Hanning, Michelle Gates, Daniel D. McCarthy, Leonard J. S. Tsuji

Bibliographic record

VenueISRN Public Health · 2012
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and InnovationCanadian Institutes of Health ResearchDanone Institute of CanadaHealth CanadaInstituto DanoneDanoneHeart and Stroke Foundation of Canada
KeywordsAlgorithmMachine learningArtificial intelligenceMathematicsComputer science

Abstract

fetched live from OpenAlex

Background. The current study established baseline nutrient intakes of youth and examined the potential for sustainability of a pilot school snack program in the remote First Nation of Kashechewan, Ontario, Canada. Methods. Twenty-four-hour dietary recalls established baseline intakes in grade 6–8 students ( n=43 ). Subsequently, a pilot healthy school snack program was initiated. Dietary recalls were completed one week ( n=43 ) and one year after the program ( n=67 ). Paired Wilcoxon signed-ranks tests were used to detect changes in intakes. Impressions of the teachers ( n=16 ), principal, and students were collected qualitatively. Results. Most youth had dietary intakes below current standards. Although vitamin C ( P=0.024 ) and fibre ( P=0.015 ) intakes improved significantly after one week, these improvements were not sustained over the following year. Program impressions were positive. Conclusion. The need for a nutrition program is clear. While benefits were realized in the short term, improvements could not be maintained. Policy changes are needed to address barriers to sustainability.

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)0.004
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 score0.155
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.337
Teacher spread0.259 · 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

Citations17
Published2012
Admission routes3
Has abstractyes

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