Catalyzing Action on First Nations Respiratory Health Using Community-based Participatory Research: Integrated Knowledge Translation through Strategic Symposia
Bibliographic record
Abstract
Assess, Redress, Re-assess: Addressing Disparities in Respiratory Health Among First Nations is an ongoing community-based participatory research initiative involving two First Nations communities in Saskatchewan. The initiative’s rationale is grounded in the ethos of transformative community-based participatory research and facilitated through integrated knowledge translation with the aim of building community capacity. The initiative’s goal was to engage community members to actively participate in all research phases, from the development of the research questions to dissemination of results and evaluation of community-chosen interventions that evolved from the results. After baseline assessment of predictors and indicators of respiratory health, a program of integrated knowledge translation was adopted. As part of this program, a community-researcher collaboration was put in place that produced two knowledge translation symposia. The two symposia have brought together First Nations community members, interdisciplinary researchers, federal and provincial policy makers, and multiple Aboriginal organizational stakeholders. The symposia provided a pathway for knowledge synthesis and sharing to ultimately integrate knowledge into practice and enable First Nations’ community capacity building in addressing and redressing critical respiratory health issues. This article delineates the processes involved in developing this model of integrated knowledge translation and highlights the continuing engagement with the participating communities supported by Knowledge Translation (KT) Symposia.
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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 | 0.238 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.006 | 0.012 |
| 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".