Implementing participatory intervention and research in communities: lessons from the Kahnawake Schools Diabetes
Bibliographic record
Abstract
Community public health interventions based on citizen and community participation are increasingly discussed as promising avenues for the reduction of health inequalities and the promotion of social justice. However, very few authors have provided explicit principles and guidelines for planning and implementing such interventions, especially when they are linked with research. Traditional approaches to public health programming emphasise expert knowledge, advanced detailed planning, and the separation of research from intervention. Despite the usefulness of these approaches for evaluating targeted narrow-focused interventions, they may not be appropriate in community health promotion, especially in Aboriginal communities. Using the experience of the Kahnawake Schools Diabetes Prevention Project, in Canada, this paper elaborates four principles as basic components for an implementation model of community programmes. The principles are: (1) the integration of community people and researchers as equal partners in every phase of the project, (2) the structural and functional integration of the intervention and evaluation research components, (3) having a flexible agenda responsive to demands from the broader environment, and (4) the creation of a project that represents learning opportunities for all those involved. The emerging implementation model for community interventions, as exemplified by this project, is one that conceives a programme as a dynamic social space, the contours and vision of which are defined through an ongoing negotiation process. r 2002 Published by Elsevier Science Ltd.
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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 | 0.098 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.029 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| 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".