Exploring Partnership Functioning Within a Community-Based Participatory Intervention to Improve Disaster Resilience
Bibliographic record
Abstract
Disasters happen worldwide, and it is necessary to engage emergency management agencies, health and social services, and community-based organizations in collaborative management activities to enhance community resilience. Community-based participatory research (CBPR) has been widely accepted in public health research as an approach to develop partnerships between academic researchers and community stakeholders and to promote innovative solutions to complex social issues. Little is known, however, about how CBPR partnerships function and contribute to successful outcomes. In this article, the authors present a case study of a CBPR partnership formed with the community of Quebec City, Canada, under the Enhancing Resilience and Capacity for Health (EnRiCH) Project, to improve emergency preparedness and adaptive capacity among high-risk populations. This qualitative study presents participants’ perspectives on how the partnership functioned and the outcomes of this collaboration. Findings are discussed in relation to contextual and group dynamics, as well as system and capacity 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 | 0.046 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| 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".