Increasing Accessibility for Community Participants at Academic Conferences
Bibliographic record
Abstract
BACKGROUND: To decrease the gulf between academic and community perspectives, there has been an increased call among stakeholder groups to collaborate in solving complex urban health problems. Despite these recent shifts, however, community partners face barriers to participate in conferences, ultimately limiting exchange of ideas and uptake of research data. This paper reports on the evaluation of the strategy used to engage community participants at the 4th International Conference on Urban Health (ICUH) held in Toronto, Canada, in October, 2005. METHOD: We surveyed participants (n = 98) and conducted follow-up interviews (n = 23) to assess factors that facilitated attendance at ICUH 2005 as well as the impact of the conference on their work. RESULTS: Community registrants were drawn by accessible fee structures, scholarship opportunities, and preconference workshops relevant to their interests. Both community and academic registrants were drawn by the presence of a separate conference stream showcasing high-quality and rigorous community-based participatory research (CBPR). The conference provided valuable opportunities for networking with other community-based researchers by facilitating the development of relationships between community registrants and researchers, increased the profile and legitimacy of CBPR, and reinforced the value of community input in research. It also provided opportunities for capacity building-knowledge sharing and heightened awareness of CBPR. CONCLUSION: The 4th ICUH had a significant impact on community registrants and provided valuable opportunities to bridge academic and community divides. These data support the need for comprehensive strategies for community engagement at health conferences.
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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 | 0.028 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".