Are we walking the talk of participatory Indigenous health research? A scoping review of the literature in Atlantic Canada
Bibliographic record
Abstract
INTRODUCTION: Participatory research involving community engagement is considered the gold standard in Indigenous health research. However, it is sometimes unclear whether and how Indigenous communities are engaged in research that impacts them, and whether and how engagement is reported. Indigenous health research varies in its degree of community engagement from minimal involvement to being community-directed and led. Research led and directed by Indigenous communities can support reconciliation and reclamation in Canada and globally, however clearer reporting and understandings of community-led research is needed. This scoping review assesses (a) how and to what extent researchers are reporting community engagement in Indigenous health research in Atlantic Canada, and (b) what recommendations exist in the literature regarding participatory and community-led research. METHODS: Eleven databases were searched using keywords for Indigeneity, geographic regions, health, and Indigenous communities in Atlantic Canada between 2001-June 2020. Records were independently screened by two reviewers and were included if they were: peer-reviewed; written in English; health-related; and focused on Atlantic Canada. Data were extracted using a piloted data charting form, and a descriptive and thematic analysis was performed. 211 articles were retained for inclusion. RESULTS: Few empirical articles reported community engagement in all aspects of the research process. Most described incorporating community engagement at the project's onset and/or during data collection; only a few articles explicitly identified as entirely community-directed or led. Results revealed a gap in reported capacity-building for both Indigenous communities and researchers, necessary for holistic community engagement. Also revealed was the need for funding bodies, ethics boards, and peer review processes to better facilitate participatory and community-led Indigenous health research. CONCLUSION: As Indigenous communities continue reclaiming sovereignty over identities and territories, participatory research must involve substantive, agreed-upon involvement of Indigenous communities, with community-directed and led research as the ultimate goal.
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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 | 0.065 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.028 | 0.059 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".