Promoting Face-to-Face Dialogue for Community Engagement in a Digital Age
Bibliographic record
Abstract
Background Health researchers in urban centers recognize the need to engage with inner-city community-based organizations. Funding for face-to-face engagement is often limited because most work done by agencies and academics now focuses on the use of digital technology. Purpose This article presents reflections from a grant project aimed at establishing community engagement between academic health researchers and interdisciplinary inner-city community health and social service providers. Method This study utilized a community-based participatory action approach. This study included a 1-day collaborative meeting to promote academic-agency engagement. During this meeting, the research participants brainstormed research priorities and used colored stickers to rank them. The research team met the following day to debrief the meeting and to begin analyzing the data together. Results The findings from this project have stimulated dialogue among the agency partners and project team researchers with respect to current collaborations, services provided, and research priorities. Although digital or virtual meetings have their place, fostering community engagement through a face-to-face meeting proved invaluable to the participants. Conclusions The success of this Canadian Institutes of Health Research-funded project demonstrates the value of academic-agency partnership, the positive aspects of gathering community, and engagement in better meeting the research needs of inner-city organizations.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".