Using diversity, equity and inclusion school practices to mitigate child health inequities in Canada
Bibliographic record
Abstract
Abstract Background The burden of unhealthy lifestyle behaviours and mental disorders disproportionately affects children in deprived neighbourhoods. In Canada, school practices addressing diversity, equity and inclusion (DEI) are promoted to address such inequities. We aimed to assess whether DEI school practices can help mitigate the impact of neighbourhood deprivation on children’s lifestyle behaviours and mental health. Methods In 2023 we gathered survey data from 1497 grade 4-6 children (aged 9-12) in 22 elementary schools located in deprived neighbourhoods in Alberta, Canada. Children reported their diet, screen time, physical activity, and mental health. Principals reported on the implementation of DEI practices in schools (full vs. partial). School postal codes were used to derive tertiles of the Canadian Index of Multiple Deprivation, comprised of four dimensions that capture: 1) residential instability; 2) economic dependency; 3) ethno-cultural composition and 4) situational vulnerability of neighbourhoods. Results Overall, 45% of schools had fully implemented DEI practices. These schools were located in more deprived neighbourhoods for residential instability (55%), economic dependency (71%), ethno-cultural composition (60%), and situational vulnerability (64%). Children in more deprived neighbourhoods reported more sugar consumption, more screen time, and poorer mental health, compared to children in less deprived neighbourhoods. However, in those neighbourhoods where DEI practices were fully implemented, the adverse effects of neighbourhood deprivation were alleviated, whereby children reported less sugar consumption, less screen time, and better mental health. Conclusions The implementation of DEI school practices can help mitigate the adverse effects of neighbourhood deprivation on children’s physical and mental health, particularly in deprived neighbourhoods. Key messages • School practices targeting diversity, equity, and inclusion are more commonly implemented in schools located in deprived neighbourhoods. • Diversity, equity, and inclusion school practices mitigate the adverse effects of neighbourhood deprivation on children’s lifestyle behaviours and mental health.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| 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".