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Record W2998799967 · doi:10.1186/s12889-019-8120-3

School-based nutrition interventions for Indigenous children in Canada: a scoping review

2020· review· en· W2998799967 on OpenAlexafffundabout
Christina Gillies, Rosanne Blanchet, Rebecca Gokiert, Anna Farmer, Jessica Thorlakson, Laura Hamonic, Noreen D. Willows

Bibliographic record

VenueBMC Public Health · 2020
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsAlberta LibraryUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionIndigenousBiostatisticsMedicinePublic healthMedical educationEnvironmental healthGerontologyFamily medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Indigenous children in Canada (First Nations, Inuit, and Métis) are disproportionately affected by nutrition-related chronic diseases such as obesity and diabetes. Comprehensive school-based nutrition interventions offer a promising strategy for improving children's access to healthy foods and sustaining positive eating behaviors. However, little is known about school-based nutrition interventions for Indigenous children. The objectives of this scoping review were to identify school-based nutrition interventions for Indigenous children in Canada and describe their components. METHODS: The scoping review consisted of searches in seven peer-reviewed databases and a general web search for grey literature. Eligibility criteria were applied by two reviewers, and data were extracted and charted by one reviewer using components of the comprehensive school health approach (social and physical environment, teaching and learning, policy, partnerships and services) and additional components with relevance to Indigenous interventions (cultural content, Indigenous control and ownership, funding source, evaluation). Numerical and descriptive summaries were used to present findings. RESULTS: Thirty-four unique interventions met the inclusion criteria. The majority (97%) of interventions targeted the social and physical environment, most often by offering food programs. Over half of interventions also incorporated teaching and learning (56%) and partnerships and services (59%), but fewer included a policy component (38%). Many interventions included a cultural component (56%) and most (62%) were owned and controlled by Indigenous communities (62%). Finally, over half of interventions disclosed their source(s) of funding (59%), but less than half (41%) included an evaluation component. CONCLUSIONS: The review suggests that school-based interventions for Indigenous children can be more comprehensive by incorporating culturally relevant nutrition education and professional development opportunities for teachers, written school nutrition policies, and activities that actively engage families and community members. The continued focus on Indigenous control and ownership and incorporation of content specific to individual communities may enhance cultural relevancy and sustainability of interventions. Furthermore, there is a need to increase intervention evaluation and the sharing of resources related to funding. These recommendations may be used by communities, as well as by researchers and professionals working with communities, in developing comprehensive school-based nutrition interventions to improve the eating behaviors of Indigenous children.

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)0.018
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.186
Threshold uncertainty score0.445

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.056
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0150.025
Science and technology studies0.0040.002
Scholarly communication0.0060.002
Open science0.0040.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.118
GPT teacher head0.431
Teacher spread0.313 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations32
Published2020
Admission routes3
Has abstractyes

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