Methodological Reflections of a Student- and Community-based Partnership on Operationalizing a Community-based Participatory Research Model: Recommendations for Building, Securing, and Sustaining Partnerships
Bibliographic record
Abstract
BACKGROUND: Community-based participatory research (CBPR) is an approach that values community expertise and ownership in creating knowledge. This approach's success is challenged by inherent cultural imbalances, making it difficult to sustain partnerships and build from what has been learned from a project as it develops. As student researchers and community members, we reflected on the challenges in CBPR and gave guidance to future novice researchers pursuing CBPR. OBJECTIVES: From the application of an initial CBPR model as a framework to our partnership, we propose empirical avenues to continuously adapt the CBPR approach. METHODS: A CBPR partnership between McGill's Family Medicine Graduate Student Society and Share the Warmth, a community-based organization aiming to fight poverty and hunger, was formed to collaboratively assess a music program offered in a socioeconomically disadvantaged community. The partnership process was based on a model that we conceptualized in three phases of our framework: building, securing, and sustaining. We reflect on the facilitators and challenges of this project and propose solutions to overcome identified barriers within the context of our partnership. RESULTS: We highlight the importance of integrating student partners in the community, reevaluating formal research agreements, and coordinating the transition of new partners in this adaptive CBPR model. We argue that this systematic and reflexive process has made the model especially useful as a framework for student and community partnerships. CONCLUSIONS: We propose adaptive components to the CBPR model. Our recommendations could help other partnerships cultivate CBPR to be more applicable in community health research.
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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 | 0.423 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.037 | 0.110 |
| Scholarly communication | 0.042 | 0.039 |
| Open science | 0.013 | 0.036 |
| Research integrity | 0.016 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".