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Record W2104527220 · doi:10.1177/1049732312443737

Using First Nations Children’s Perceptions of Food and Activity to Inform an Obesity Prevention Strategy

2012· article· en· W2104527220 on OpenAlexafffundabout
Ashlee-Ann Pigford, Noreen D. Willows, Nicholas L. Holt, Amanda S. Newton, Geoff D.C. Ball

Bibliographic record

VenueQualitative Health Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsSociocultural evolutionFocus groupPerceptionInterdependencePsychologyAffect (linguistics)Food choiceObesityPreferenceHealth educationDevelopmental psychologyMedicineEnvironmental healthGerontologySociologyPublic healthSocial scienceNursing

Abstract

fetched live from OpenAlex

Obesity and associated health risks disproportionately affect Aboriginal (First Nations) children in Canada. The purpose of this research study was to elicit First Nations children's perceptions of food, activity, and health to inform a community-based obesity prevention strategy. Fifteen 4th- and 5th-Grade students participated in one of three focus group interviews that utilized drawing and pile-sorting activities. We used an ecological lens to structure our findings. Analyses revealed that a variety of interdependent sociocultural factors influenced children's perceptions. Embedded within a cultural/traditional worldview, children indicated a preference for foods and activities from both contemporary Western and traditional cultures, highlighted family members as their main sources of health information, and described information gaps in their health education. Informed by children's perspectives, these findings offer guidance for developing an obesity prevention strategy for First Nations children in this community.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.771

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.406
GPT teacher head0.604
Teacher spread0.198 · 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

Citations27
Published2012
Admission routes3
Has abstractyes

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