Impact of Non-Pharmacological Interventions in Indigenous Populations with Diabetes Mellitus on Cardiovascular and Kidney Disease: A Scoping Review Using the REAIM Framework
Bibliographic record
Abstract
Background: Diabetes mellitus is a common cause of mortality from cardiovascular (CV) and kidney diseases. This scoping review utilized the RE-AIM (reach, efficacy, adoption, implementation, and maintenance) framework to assess the impact of nonpharmacological interventions on CV and kidney health outcomes (KHO) in Indigenous populations Methods: We searched Medline, Embase, Cochrane Library, CINAHL, Web of Science, PsycINFO and other grey literature to identify studies that used nonpharmacological interventions (exercise, nutrition, telehealth, educational, health worker, and cultural) to achieve improved glycaemic control, and reduction of clinical or laboratory markers of CV or KHO in Indigenous communities Results: Our search yielded 7,692 studies, from which 35 studies were selected. Culturally appropriate interventions were mostly utilized (77.1%); telehealth programs were least utilized (8.6%). Clinical and laboratory indices of CV and KHO were infrequently assessed (KHO assessed in 40%); improved kidney function was reported in 10.5% of health worker interventions. (Table 1). Reporting of items of the RE-AIM framework showed that internal validity items were more frequently reported than those of external validity: reach (60%), efficacy (52.1%), adoption (46.1%), implementation (41.9%), and maintenance (37.2%) (Table 2)Table 1Table 2Conclusions: Due to the high prevalence of CV and kidney diseases in diabetic patients of Indigenous groups, studies using diabetes interventions need to report more items of external validity to allow the findings of such interventions to be translatable into practice
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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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".