Traditional, complementary and integrative medicine use among Indigenous peoples with diabetes in Australia, Canada, New Zealand and the United States
Bibliographic record
Abstract
OBJECTIVE: This systematic review aimed to describe traditional, complementary and integrative medicine (TCIM) use among Indigenous peoples with diabetes from Australia, Canada, New Zealand and the United States (US). METHODS: A systematic search following the PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses) statement guidelines was conducted. Data were analysed using meta-aggregation. RESULTS: Thirteen journal articles from 12 studies across Australia, Canada and the US were included in the review (no articles from New Zealand were identified). Indigenous peoples used various types of TCIM alongside conventional treatment for diabetes, particularly when conventional treatment did not meet Indigenous peoples' holistic understandings of wellness. TCIM provided opportunities to practice important cultural and spiritual activities. While TCIM was often viewed as an effective treatment through bringing balance to the body, definitions of treatments that comprise safe and effective TCIM use were lacking in the articles. CONCLUSIONS: The concurrent use of TCIM and conventional treatments is common among Indigenous peoples with diabetes, but clear definitions of safe and effective TCIM use are lacking. Implications for public health: Healthcare providers should support Indigenous peoples to safely and effectively treat diabetes with TCIM alongside conventional treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".