Community Knowledge Mobilization and the Community Readiness Model Tool: A case study of diabetes in selected First Nations Communities in Canada
Bibliographic record
Abstract
Background Chronic disease, such as type 2 diabetes, is a large concern for public health within developed nations, especially in First Nations communities, where rates are much higher than in non-Aboriginal populations. Successful interventions are always well tailored to their context, and participatory rural appraisal methods aid in this process of adaptation. Community readiness assessment is one example of how knowledge about the community landscape and context is created and mobilized for action. Despite the accessibility of this knowledge, there has been a large hurdle in implementing successful and sustainable programs and activities at the community level. Knowledge mobilization and knowledge translation strategies have the potential to in-crease mobilization within communities for prevention activities. Objective To better understand the role of a research-developed tool for knowledge mobilization processes in First Nations communities. Of special interest is how the adapted Community Readiness Model (CRM) tool creates knowledge that can then be translated to action from the perspective of lay First Nations community members.Methods Qualitative descriptive study based of focus group discussion.Participants Community members from 6 First Nations communities recruited within Wave 1 of the FORGE-AHEAD research program. Results Participants discussed the usefulness of the CRM tool around the following themes: key informants, community context, leadership, structure and organization of the tool, and mobilization using the Knowledge to Action (KTA) framework. Conclusion Interpreting mobilization through the lens of the KTA cycle as a programming model highlighted the value of the CRM tool to the community, through providing knowledge about the community context, and acting as a catalyst for forward momentum down their mobilization paths. With a few adaptations to better suit a First Nations community context, CRM tool would make an excellent contribution to the Participatory Rural Appraisal toolkit.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".