Engaging Participants Through Hybrid Community-Centered Approaches: Lessons Learned During the COVID CommUNITY Public Health Research Program
Bibliographic record
Abstract
Community-centered research studies can improve trust, cultural appropriateness, and accurate findings through meaningful, in-depth engagement with participants. During the COVID-19 pandemic, researchers shifted to implement pandemic-specific guidelines on top of already existing safety practices; these adjustments gave insight into bettering the structure of forthcoming research studies. At the Population Health Research Institute (PHRI)/McMaster University, the COVID CommUNITY study staff took field notes from their experience at the Ontario (ON) and British Columbia (BC) sites navigating an observational prospective cohort study during the pandemic. These field notes are outlined below to provide insight into culturally responsive, trust-centered, and communication-focused strategies used to improve hybrid research. A significant challenge the team overcame was obtaining blood sample collections by executing socially distanced sample collections outside of participants' homes, coined "Porch Pickups." Data collection was made more accessible through phone surveys and frequent virtual contact. To enhance recruitment strategies for sub-communities of the South Asian population, staff focused on cultural interests and "gift-exchange" incentives. Cultural awareness was prioritized through correct name pronunciation, conducting data collection in participant preferred languages, and using flexible approaches to data collection. These strategies were developed through weekly team meetings where improvement strategies were discussed, and concerns were addressed in real-time.
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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 | 0.349 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.019 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.013 | 0.039 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".