Comprehensive Nutrition Interventions in First Nation-Operated Schools in Canada
Bibliographic record
Abstract
Comprehensive school-based nutrition interventions offer a promising strategy to support healthy eating for First Nations children. A targeted strategic review was performed to identify nutrition interventions in 514 First Nation-operated schools across Canada through their websites. Directed content analysis was used to describe if interventions used 1 or more of the 4 components of the Comprehensive School Health (CSH) framework. Sixty schools had interventions. Nearly all (n = 56, 93%) schools offered breakfast, snack, and (or) lunch programs (social and physical environment). About one-third provided opportunities for students to learn about traditional healthy Indigenous foods and food procurement methods (n = 18, 30%) (teaching and learning) or facilitated connections between the school and students' families or the community (n = 16, 27%) (partnerships and services). Few schools (n = 10, 17%) had a nutrition policy outlining permitted foods (school policy). Less than 1% (n = 3) of interventions included all 4 CSH components. Results suggest that most First Nation-operated schools provide children with food, but few have nutrition interventions that include multiple CSH components. First Nation-operated schools may require additional financial and (or) logistical support to implement comprehensive school-based nutrition interventions, which have greater potential to support long-term health outcomes for children than single approaches.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".