An Integrative Review on the Effect of School- Based Nutrition Programs on Obesity Rates in Indigenous Youth
Bibliographic record
Abstract
An Integrative Review on the Effect of School- Based Nutrition Programs on Obesity Rates in Indigenous Youth\nBackground\nObesity-related diseases are disproportionately pervasive within Indigenous communities in Canada (King et al., 2009). Obesity is linked to various chronic illnesses which lead to decreases in quality of life, loss of ability to work, increased morbidity and mortality, and a significant burden and financial strain on the healthcare system (MacEwan et al. 2011). Individuals under 15 years of age account for a greater proportion of the population within Indigenous communities; therefore, children are especially demographically significant in this context. School-based nutritional programs show promise in affecting positive change related to healthy eating behaviours amongst Indigenous children. However, there are no well-established guidelines or protocols related to these interventions, hence, the aim of this integrative review is to identify the most effective strategies in reducing obesity rates amongst Indigenous youth in Canada.\nMethods\nTo date, this integrative review has consisted of searches in three peer-reviewed databases and a general web search for grey literature. Eligibility criteria were applied by three reviewers, and data were extracted and charted by six reviewers using components of culturally relevant education, increasing access to nutritious food choices, Indigenous ownership, and implementing physical activity programs.\nResults\nThe literature review is ongoing. Preliminary results indicate school-based nutrition programs improve healthy eating knowledge and behaviours when paired with supplemental education regarding nutritional choices, increasing physical activity, and reducing sedentary behaviours (Valery et al., 2021). Combining immediate and long-term initiatives to combat the obesity crisis is important to decrease health disparities amongst Canadian Indigenous youth. \nConclusion\nThrough this integrative review, we hope to identify components of the most successful nutritional interventions within Indigenous communities in Canada. This will help guide the planning, development, and implementation of future nutrition programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".