Re-Visioning Community Mobilization Training: Centering Indigenous Knowledge, Culture, and Self-Determination
Bibliographic record
Abstract
Community mobilization is a self-determined process where communities draw on their culture, strengths, and resources to advance shared priorities for wellness. The Kahnawà:ke Schools Diabetes Prevention Program (KSDPP) is a long-standing example, recognized for its success in community health promotion. The Community Mobilization Training (CMT) was developed from the KSDPP model to share best practices for mobilization with communities across Turtle Island. The original CMT integrated Indigenous values and culture with Western planning models, including the PRECEDE–PROCEED framework, the Ottawa Charter for Health Promotion, and Social Cognitive Theory. While these models offer useful constructs, over time, both communities and research team members have recognized that privileging these Western frameworks within the CMT overshadowed Indigenous values that were implicitly present and essential. The time has come to strengthen the community-based foundations of the training and shift away from these models, leading instead with Indigenous culture, values, epistemologies, and practices, placing community self-determination at its core. In response, we are revitalizing the CMT to be explicitly grounded in Indigenous knowledge systems, while retaining Western program planning, implementation, and evaluation concepts where they align with Indigenous approaches and are valued by communities. Guided by Indigenous knowledge translation frameworks (e.g., Smylie et al.), our approach emphasizes respect, relationship, and reciprocity, ensuring that the CMT remains culturally resonant and supportive of community-led health promotion. Our re-visioning is informed by multiple data sources: talking circles on cultural grounding; surveys assessing community readiness; and interviews with researchers, participants and facilitators. Using a realist evaluation approach, we are developing a program theory to describe how mobilization unfolds when driven by Indigenous ways of knowing and doing. By rooting the training in Indigenous strengths and knowledges, the revised CMT better captures the realities of communities and the central role of culture in driving collective wellness across Turtle Island.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.013 |
| 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".