A socioecological framework to understand weight-related issues in Aboriginal children in Canada
Bibliographic record
Abstract
Obesity prevention efforts in Aboriginal (First Nations, Métis, or Inuit) communities in Canada should focus predominantly on children given their demographic significance and the accelerated time course of occurrence of type 2 diabetes mellitus in the Aboriginal population. A socioecological model to address childhood obesity in Aboriginal populations would focus on the numerous environments at different times in childhood that influence weight status, including prenatal, sociocultural, family, and community environments. Importantly, for Aboriginal children, obesity interventions need to also be situated within the context of a history of colonization and inequities in the social determinants of health. This review therefore advocates for the inclusion of a historical perspective and a life-course approach to obesity prevention in Aboriginal children in addition to developing interventions around the socioecological framework. We emphasize that childhood obesity prevention efforts should focus on promoting maternal health behaviours before and during pregnancy, and on breastfeeding and good infant and child nutrition in the postpartum and early childhood development periods. Ameliorating food insecurity by focusing on improving the sociodemographic risk factors for it, such as increasing income and educational attainment, are essential. More research is required to understand and measure obesogenic Aboriginal environments, to examine how altering specific environments modifies the foods that children eat and the activities that they do, and to examine how restoring and rebuilding cultural continuity in Aboriginal communities modifies the many determinants of obesity. This research needs to be done with the full participation of Aboriginal communities as partners in the research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".