Implementation framework for chronic disease intervention effectiveness in Māori and other indigenous communities
Bibliographic record
Abstract
BACKGROUND: About 40% of all health burden in New Zealand is due to cancer, cardiovascular disease, and type 2 diabetes/obesity. Outcomes for Māori (indigenous people) are significantly worse than non-Maori; these inequities mirror those found in indigenous communities elsewhere. Evidence-based interventions with established efficacy may not be effective in indigenous communities without addressing specific implementation challenges. We present an implementation framework for interventions to prevent and treat chronic conditions for Māori and other indigenous communities. THEORETICAL FRAMEWORK: The He Pikinga Waiora Implementation Framework has indigenous self-determination at its core and consists of four elements: cultural-centeredness, community engagement, systems thinking, and integrated knowledge translation. All elements have conceptual fit with Kaupapa Māori aspirations (i.e., indigenous knowledge creation, theorizing, and methodology) and all have demonstrated evidence of positive implementation outcomes. APPLYING THE FRAMEWORK: A coding scheme derived from the Framework was applied to 13 studies of diabetes prevention in indigenous communities in Australia, Canada, New Zealand, and the United States from a systematic review. Cross-tabulations demonstrated that culture-centeredness (p = .008) and community engagement (p = .009) explained differences in diabetes outcomes and community engagement (p = .098) explained difference in blood pressure outcomes. IMPLICATIONS AND CONCLUSIONS: The He Pikinga Waiora Implementation Framework appears to be well suited to advance implementation science for indigenous communities in general and Māori in particular. The framework has promise as a policy and planning tool to evaluate and design effective interventions for chronic disease prevention in indigenous communities.
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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 | 0.151 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".