Lessons learned from a virtual Community-Based Participatory Research project: prioritizing needs of people who have diabetes and experiences of homelessness to co-design a participatory action project
Bibliographic record
Abstract
In Community-Based Participatory Research (CBPR), people with shared lived experiences (co-researchers) identify priority needs and work collaboratively to co-design an action-oriented research advocacy project. For this to occur, academic researchers must build mutually respectful partnerships with co-researchers by establishing trust. In the context of the COVID-19 pandemic, our objective was to virtually assemble a group of co-researchers (people with diverse but relevant experiences of homelessness and diabetes) and academic researchers who engaged in the CBPR process to identify a project that would address the difficulties of diabetes management while experiencing homelessness. Co-researchers were recruited to the committee from community homeless-serving organizations. Six co-researchers, one peer researcher and three academic researchers from Calgary, Alberta met virtually for bi-weekly committee meetings, from June 2021 to May 2022 to explore barriers to diabetes management and to complete a priority-setting exercise to determine the focus of our collective project. After reflecting on our virtual CBPR experience we present lessons learned related to: i) technical challenges and logistical considerations, ii) meeting virtually and building rapport, iii) driving engagement, and iv) challenges of transitioning from virtual to in-person meeting format. Overall, the process of conducting a CBPR project virtually to engage a group of co-researchers during a pandemic presents its challenges. However, a virtual CBPR project is feasible and can lead to meaningful experiences that benefit all group members, both from the community and academia.
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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 | 0.130 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.008 | 0.013 |
| 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".