Knowledge translation approaches and practices in Indigenous health research: A systematic review
Bibliographic record
Abstract
Knowledge translation (KT) is a critical component of any applied health research. Indigenous Peoples' health research and KT largely continues to be taught, developed, designed, regulated, and conducted in ways that do not prioritize local Indigenous Peoples' ways of sharing knowledges. This review was governed and informed by Indigenous health scholars, Knowledge Guardians, and Elders. Our systematic review focused on answering, what are the promising and wise practices for KT in the Indigenous health research field? Fifty-one documents were included after screening published literature from any country and grey literature from what is now known as Canada. This included contacting 73 government agencies at the federal, territorial, and provincial levels that may have funded Indigenous health research. Only studies that: a) focused on Indigenous Peoples' health and wellness; b) documented knowledge sharing activities and rationale; c) evaluated the knowledge sharing processes or outcomes; and d) printed in English were included and appraised using the Well Living House quality appraisal tool. The analysis was completed using an iterative and narrative synthesis approach. Our systematic review protocol has been published elsewhere. We highlight and summarize the varied aims of Indigenous health research KT, types of KT methodologies and methods used, effectiveness of KT efforts, impacts of KT on Indigenous Peoples' health and wellness, as well as recommendations and lessons learned. Few authors reported using rigorous KT evaluation or disclosed their identity and relationship with the Indigenous communities involved in research (i.e. self-locate). The findings from this review accentuate, reiterate and reinforce that KT is inherent in Indigenous health research processes and content, as a form of knowing and doing. Indigenous health research must include inherent KT processes, if the research is by, for, and/or with Indigenous Peoples.
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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 | 0.097 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".