The need for diabetes interventions to have a life course perspective rooted in critical decolonizing Indigenous theory
Bibliographic record
Abstract
Diabetes is a multifaceted disease, with a myriad of causes. Diabetes has been linked to reducing lifespan 5-15 years, contributing to: cardiovascular disease, renal failure, amputation, stroke, heart attack and blindness. Diabetes currently affects 3.4 million Canadians, with that number projected to increase to 5 million in 2025 (CNIB, 2015). Diabetes has been shown to affect Indigenous populations disproportionately from the rest of the Canadian populations, with those living on First Nation reserves having a diagnosis rate 3-5 times higher (PHAC, 2011). The purpose of this review is to critically analyze diabetes interventions that have been initiated on First Nation reserves in Canada and evaluate their effectiveness, while advocating for interventions to use ideas derived from life course theory and critical decolonizing Indigenous theory to target children. Life course theory posits that development occurs over the lifespan within social and historical constraints, and with respect to diabetes, can begin to manifest through key events stemming from as early as gestation (Elder, Johnson and Crosnoe, 2003; Hertzman and Power, 2006). Critical decolonizing theory, in part, looks to identify the ways colonization has impacted the health of Indigenous peoples, while advocating for an understanding of traditional Indigenous health views (Smylie, Kaplan-Myrth and McShane, 2009). This review will consist of a comprehensive search of ProQuest, Google Scholar and Pubmed, in order to identify what initiatives are being implemented in First Nation communities across Canada. Using a life course approach to create initiatives that target children will enable maximum effectiveness in reducing the likelihood of diabetes diagnosis in later years. In addition, employing decolonized culturally and socially relevant methodology will allow for diabetes interventions to resonate with First Nations populations in ways that will have lasting impacts.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".