Knowledge, capacity and readiness: translating successful experiences in community-based participatory research for health promotion
Bibliographic record
Abstract
Capacity building is a guiding principle of community-based participatory research (CBPR). This paper explores the interrelationship between capacity building and the concepts of readiness and intercommunity knowledge translation. A five-year study examined two long-standing projects for the primary prevention of type 2 diabetes in Aboriginal communities, to translate the lessons learned from those experiences into capacity for diabetes prevention in a third Aboriginal community. Reviewing external factors with the PRECEDE-PROCEED model of health promotion reveals that readiness for change requires both intra- and extra-community enabling factors including expertise from other communities, national and international organizations, federal health service funding, available research and intervention funding, and availability of external partners. These resources do not address the community health issue directly, but rather build capacity, objective and environmental, for the community to address the issue itself. It was found that a community that is internally ready, and situated within an external enabling environment rich in appropriate resources, can translate the knowledge from other successful community experiences to develop the capacity to initiate community health promotion for diabetes prevention.
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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 | 0.223 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".