The impact of community-based non-pharmacological interventions on cardiovascular and kidney disease outcomes in remote dwelling Indigenous communities: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Indigenous people represent approximately 5% of the world's population. However, they often have a disproportionately higher burden of cardiovascular disease (CVD) risk and chronic kidney disease (CKD) than their equivalent general population. Several non-pharmacological interventions (e.g., educational) have been used to reduce CVD and kidney disease risk factors in Indigenous groups. The aim of this paper is to describe the protocol for a scoping review that will assess the impact of non-pharmacological interventions carried out in Indigenous and remote dwelling populations to reduce CVD risk factors and CKD. MATERIALS AND METHODS: This scoping review will be guided by the methodological framework for conducting scoping studies developed by Arksey and O'Malley. Both empirical (Medline, Embase, Cochrane Library, CINAHL, ISI Web of Science and PsycINFO) and grey literature references will be assessed if they focused on interventions targeted at reducing CVD or CKD among Indigenous groups. Two reviewers will independently screen references in consecutive stages of title/abstract screening and then full-text screening. Impact of interventions used will be assessed using the reach, effectiveness, adoption, implementation, maintenance (RE-AIM) framework. A descriptive overview, tabular summaries, and content analysis will be carried out on the extracted data. ETHICS AND DISSEMINATION: This review will collect and analyse evidence on the impact of interventions of research carried out to reduce CVD and CKD among Indigenous populations. Such evidence will be disseminated using traditional approaches that includes open-access peer-reviewed publication, scientific presentations, and a report. Also, we will disseminate our findings to the government and Indigenous leaders. Ethical approval will not be required for this scoping review as the data used will be extracted from already published studies with publicly accessible data.
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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 | 0.129 | 0.107 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.012 |
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".