Knowledge translation in Indigenous health research: voices from the field
Bibliographic record
Abstract
OBJECTIVES: To better understand what knowledge translation activities are effective and meaningful to Indigenous communities and what is required to advance knowledge translation in health research with, for, and by Indigenous communities. STUDY DESIGN: Workshop and collaborative yarning. SETTING: Lowitja Institute International Indigenous Health Conference, Cairns, June 2023. PARTICIPANTS: About 70 conference delegates, predominantly Indigenous people involved in research and Indigenous health researchers who shared their knowledge, experiences, and recommendations for knowledge translation through yarning and knowledge sharing. RESULTS: Four key themes were developed using thematic analysis: knowledge translation is fundamental to research and upholding community rights; knowledge translation approaches must be relevant to local community needs and ways of mobilising knowledge; researchers and research institutions must be accountable for ensuring knowledge translation is embedded, respected and implemented in ways that address community priorities; and knowledge translation must be planned and evaluated in ways that reflect Indigenous community measures of success. CONCLUSION: Knowledge translation is fundamental to making research matter, and critical to ethical research. It must be embedded in all stages of research practice. Effective knowledge translation approaches are Indigenous-led and move beyond Euro-Western academic metrics. Institutions, funding bodies, and academics should embed structures required to uphold Indigenous knowledge translation. We join calls for reimaging health and medical research to embed Indigenous knowledge translation as a prerequisite for generative knowledge production that makes research matter.
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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 | 0.208 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.056 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.018 | 0.027 |
| 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".