Increased community engagement of Indigenous Peoples in dementia research leads to higher context relevance of results
Bibliographic record
Abstract
INTRODUCTION: Health research that focuses on Indigenous Peoples must ensure that the community in question is actively engaged, and that the results have context relevance for Indigenous Peoples. Context relevance is "the benefits, usability, and respectful conduct of research from the perspective of Indigenous communities." The purpose of this study was to apply two tools within an already-published scoping review of 76 articles featuring research on cognitive impairment and dementia among Indigenous Peoples worldwide. One tool assessed levels of community engagement reported in the corpus, and the other tool assessed the context relevance of recommendations in the corpus. We hypothesized that research with higher levels of reported community engagement would produce recommendations with greater context relevance for Indigenous Peoples. METHODS: We employed semi-structured deductive coding using two novel tools assessing levels of reported community engagement and context relevance of recommendations based on studies included in the existing scoping review. RESULTS: Application of the two tools revealed a positive relationship between increasing community engagement and greater context relevance. Community engagement primarily occurred in studies conducted with First Nations, Inuit, and Métis populations in Canada and with Australian Aboriginal and/or Torres Strait Islander Peoples. Research with Alaska Native, American Indian, and Native Hawaiian Peoples in the USA stood out for its comparative lack of meaningful community engagement. DISCUSSION: There is opportunity to utilize these tools, and the results of this assessment, to enhance training and mentorship for researchers who work with Indigenous populations. There is a need to increase investigator capacity to involve communities throughout all phases of research, particularly in the pre-research stages.
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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 | 0.162 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".