Mitigating child health inequalities through equity, diversity, inclusion, and accessibility school practices in Canada
Bibliographic record
Abstract
Unhealthy lifestyle behaviours and mental disorders disproportionately affect children living in deprived neighbourhoods. In Canada, schools are encouraged to adopt equity, diversity, inclusion, and accessibility (EDIA) practices to tackle these inequalities. We examined whether integrating EDIA school practices into curriculum and programming mitigates the impact of neighbourhood deprivation on children's lifestyle behaviours and mental health. In 2023/2024, we surveyed 1970 students in grades 4-6 (aged 9-12) from 28 elementary schools in Alberta. Students self-reported diet, physical activity, screen time, and mental health and wellbeing. School principals reported on the extent (full vs. partial) of integrating EDIA school practices into curriculum and programming. The Canadian Index of Multiple Deprivation (i.e. residential instability, economic dependency, ethno-cultural composition, situational vulnerability) was used to capture neighbourhood deprivation. Over half (54%) of schools had fully integrated EDIA school practices into curriculum and programming, and were located primarily in areas with greater residential instability (50%), ethno-cultural diversity (57%), economic dependency and situational vulnerability (46%). In highly deprived neighbourhoods, students attending schools with fully integrated EDIA practices were less likely to have poor diets (0.9 vs. 1.6) and consume excessive intake of free sugar (1.3 vs. 1.8) and saturated fat (0.6 vs. 0.8). EDIA school practices did not appear to moderate the relationship of neighbourhood deprivation with physical activity, screen time, or mental health and wellbeing. These findings suggest that integrating EDIA school practices into curriculum and programming may help buffer some adverse effects of neighbourhood deprivation on children's health and diets in particular.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".