The Importance of Explicit and Timely Knowledge Exchange Practices Stemming from Research with Indigenous Families
Bibliographic record
Abstract
Ethical research practice within community-based research involves many dimensions, including a commitment to return results to participants in a timely and accessible fashion. Often, current Indigenous community-based research is driven by a partnership model; however, dissemination of findings may not always follow this approach. As a result, products may not be as useful to participants who were motivated to be involved in the research process. We conducted a seven-week workshop on three occasions with different First Nations and Metis women and girls (age 8-12) in Winnipeg, Manitoba. The workshop explored participants’ perspectives around health, safety, and family wellbeing using a strength-based, participatory approach. Participants noted that a key challenge they face when interacting with researchers, policy makers, and program staff is the lack of tailored dissemination materials. Returning results in a format that meets the expressed desire of participants is an ethical necessity to ensure that research is not perpetuating past colonial practices. Doing so quickly and with meaningful content requires careful execution and consideration, especially when working within intergenerational contexts. We describe in this paper how results were returned to families in an accessible way outlining the role that integrated knowledge exchange can play in the process of healing.
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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 | 0.301 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.010 |
| 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".