Availability and Implementation Characteristics of Nutrition Health Promotion Interventions in Quebec Elementary Schools
Bibliographic record
Abstract
BACKGROUND: Availability and quality of nutrition-related health-promoting interventions (N-HPIs) vary across primary schools. We examined whether school contextual factors (e.g., socioeconomic deprivation) were associated with N-HPI availability in Quebec, Canada, and whether available N-HPIs incorporated evidence-based implementation characteristics. METHODS: In a cross-sectional study (2016-2019), informants from 171 primary schools reported on N-HPI availability. Availability was analyzed in relation to 10 school-level contextual indicators. A subset of 52 N-HPIs was examined in-depth for alignment with 15 evidence-based implementation characteristics identified in a literature review. RESULTS: N-HPIs were reported in 120 schools (70%, including 77% serving disadvantaged populations). Among the 52 N-HPIs examined in-depth, over 75% demonstrated four core characteristics: Staff involvement, integration of multiple core competencies, innovative teaching strategies, and alignment with school context. Fewer HPIs included formal evaluation (46%), were delivered over multiple sessions (35%), or engaged students in design or implementation (15%). IMPLICATIONS: To strengthen N-HPIs, policymakers should support flexible, theory-informed interventions that incorporate evaluation from the outset. Greater involvement of students, staff, and families in planning may help address persistent barriers. CONCLUSION: While N-HPIs are widespread and often incorporate evidence-based implementation characteristics, there is room to enhance student participation, extend program duration, and strengthen evaluation.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".