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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".