Community Engagement Approaches for Indigenous Health Research: an Integrative Review
Bibliographic record
Abstract
Abstract Background Community engagement practices in Indigenous Health research are promoted as a means of decolonizing research, but there is no comprehensive synthesis of approaches in the literature. Our aim was to assemble and qualitatively synthesize a comprehensive list of actionable recommendations to enhance community engagement practices with Indigenous Peoples.MethodsWe performed an integrative review of literature in medical (Medline, CINAHL and Embase), as well as Google and World Health Organization databases (search cutoff date November 17, 2018). Studies that contained details regarding Indigenous community engagement frameworks, principles or practices in the field of health were included, with exclusion of non-English publications. Two reviewers independently screened the articles in duplicate and reviewed full text articles. Recommendations for community engagement approaches were extracted and thematically synthesized through content analysis.Results A total of 52 studies were included in the review, with 1268 individual recommendations extracted. These were synthesized into a list of 37 recommendations for community engagement approaches in Indigenous health research, categorized by stage of research. In addition, activities applicable to all phases of research were identified: partnership and trust building, and active reflection.Conclusions We provide a comprehensive list of recommendations for Indigenous community engagement approaches in health research. A limitation of this review is that it may not address all aspects applicable to specific Indigenous community settings and contexts. We encourage anyone who does research with Indigenous communities to reflect upon their practices, encouraging changes in research processes that are strengths-based.
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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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".