Community engagement approaches for Indigenous health research: recommendations based on an integrative review
Bibliographic record
Abstract
OBJECTIVE: Community engagement practices in Indigenous health research are promoted as a means of decolonising research, but there is no comprehensive synthesis of approaches in the literature. Our aim was to assemble and qualitatively synthesise a comprehensive list of actionable recommendations to enhance community engagement practices with Indigenous peoples in Canada, the USA, Australia and New Zealand. DESIGN: Integrative review of the literature in medical (Medline, Cumulative Index to Nursing and Allied Health Literature and Embase) and Google and WHO databases (search cut-off date 21 July 2020). ARTICLE SELECTION: 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. ANALYSIS: Recommendations for community engagement approaches were extracted and thematically synthesised through content analysis. RESULTS: A total of 63 studies were included in the review, with 1345 individual recommendations extracted. These were synthesised into a list of 37 recommendations for community engagement approaches in Indigenous health research, categorised 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 on their practices, encouraging changes in research processes that are strengths based.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.028 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".