A realist evaluation of community-based participatory research: partnership synergy, trust building and related ripple effects
Bibliographic record
Abstract
BACKGROUND: Community-Based Participatory Research (CBPR) is an approach in which researchers and community stakeholders form equitable partnerships to tackle issues related to community health improvement and knowledge production. Our 2012 realist review of CBPR outcomes reported long-term effects that were touched upon but not fully explained in the retained literature. To further explore such effects, interviews were conducted with academic and community partners of partnerships retained in the review. Realist methodology was used to increase the understanding of what supports partnership synergy in successful long-term CBPR partnerships, and to further document how equitable partnerships can result in numerous benefits including the sustainability of relationships, research and solutions. METHODS: Building on our previous realist review of CBPR, we contacted the authors of longitudinal studies of academic-community partnerships retained in the review. Twenty-four participants (community members and researchers) from 11 partnerships were interviewed. Realist logic of analysis was used, involving middle-range theory, context-mechanism-outcome configuration (CMOcs) and the concept of the 'ripple effect'. RESULTS: The analysis supports the central importance of developing and strengthening partnership synergy through trust. The ripple effect concept in conjunction with CMOcs showed that a sense of trust amongst CBPR members was a prominent mechanism leading to partnership sustainability. This in turn resulted in population-level outcomes including: (a) sustaining collaborative efforts toward health improvement; (b) generating spin-off projects; and (c) achieving systemic transformations. CONCLUSION: These results add to other studies on improving the science of CBPR in partnerships with a high level of power-sharing and co-governance. Our results suggest sustaining CBPR and achieving unanticipated benefits likely depend on trust-related mechanisms and a continuing commitment to power-sharing. These findings have implications for building successful CBPR partnerships to address challenging public health problems and the complex assessment of outcomes.
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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 | 0.554 | 0.596 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".