Systematic synthesis of intersectional best practices: knowledge translation for circumpolar indigenous disability
Bibliographic record
Abstract
Numerous theories, models, and frameworks (TMFs) currently exist for knowledge translation (KT), with scholarship that is increasingly inclusive of populations experiencing health inequalities. This study proposes two objectives: 1) exploring a nine-step method for synthesising best practices, acknowledging existing syntheses in the form of tailored-databases and review-style publications; and 2) collating best practices to inform KT that is inclusive to indigenous individuals living with disabilities in circumpolar regions. The resulting synthesis emphasises 10 best practices: explicitly connect the accountability of stakeholders to the wellbeing of the people they serve; recognise entanglement with existing neoliberal systems; assess impacts of KT on indigenous treatment providers; employ personal outreach visits; rectify longstanding delegitimization; avoid assuming the target group to be homogeneous, critically examine inequitable distribution of benefits and risks; consider how emphasis on a KT initiative can distract from historical and systemic inequalities; target inequitable, systemic social and economic forces; consider how KT can also be mobilised to gain power and control; assess what is selected for KT, and how it intersects with power position of external stakeholders and internal champions; and, allow people access-to-knowledge which changes inequitable systems.
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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 | 0.354 | 0.546 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".