Understanding community-based participatory research through a social movement framework: a case study of the Kahnawake Schools Diabetes Prevention Project
Bibliographic record
Abstract
BACKGROUND: A longstanding challenge of community-based participatory research (CBPR) has been to anchor evaluation and practice in a relevant theoretical framework of community change, which articulates specific and concrete evaluative benchmarks. Social movement theories provide a broad range of theoretical tools to understand and facilitate social change processes, such as those involved in CBPR. Social movement theories have the potential to provide a coherent representation of how mobilization and collective action is gradually developed and leads to systemic change in the context of CBPR. The current study builds on a social movement perspective to assess the processes and intermediate outcomes of a longstanding health promotion CBPR project with an Indigenous community, the Kahnawake Schools Diabetes Prevention Project (KDSPP). METHODS: This research uses a case study design layered on a movement-building evaluation framework, which allows progress to be tracked over time. Data collection strategies included document (scientific and organizational) review (n = 51) and talking circles with four important community stakeholder groups (n = 24). RESULTS: Findings provide an innovative and chronological perspective of the evolution of KSDPP as seen through a social movement lens, and identify intermediate outcomes associated with different dimensions of movement building achieved by the project over time (mobilization, leadership, vision and frames, alliance and partnerships, as well as advocacy and action strategies). It also points to areas of improvement for KSDPP in building its potential for action. CONCLUSION: While this study's results are directly relevant and applicable to the local context of KSDPP, they also highlight useful lessons and conclusions for the planning and evaluation of other long-standing and sustainable CBPR initiatives. The conceptual framework provides meaningful benchmarks to track evidence of progress in the context of CBPR. Findings from the study offer new ways of thinking about the evaluation of CBPR projects and their progress by drawing on frameworks that guide other forms of collective action.
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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 | 0.044 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.025 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".