CULTIVATING TRUST IN RESEARCH PARTNERSHIPS WITH OLDER ADULTS ACROSS THE COMMUNITY ENGAGEMENT CONTINUUM
Bibliographic record
Abstract
Abstract Community-engaged research is essential for identifying and supporting the needs of an aging society and ensuring effective translation of research findings. While not a new idea to the field of gerontology, engaged research models and frameworks are expanding, and researchers are seeing increased access to initiatives for building and sustaining community-engaged research programs. This symposium will feature emerging and established projects across multiple levels of the continuum of engagement, from informing, consulting, involving, collaborating, and ultimately to co-creating. Kim et al. will describe efforts to co-create programming and educational materials with and for Alaska Native caregivers, focusing on decolonizing methodologies. Halvorsen will showcase an ongoing academic-organizational partnership created to increase the research and evaluation capacity of a national nonprofit organization and its grantees. Gan will describe how diverse community members were engaged to understand and meet gaps in cognitive health promotion, resulting in the pilot of a mindful discussion program. Guest et al. will present on the formation and fruitful efforts of a resident advisory board at the University Based Retirement Community, sharing best practices for sustaining engagement. Mahmood et al. will report on a community-based participatory effort evaluating the role of built environment on mobility access across five cities in the Vancouver area. These exemplars will launch the symposium into a discussion about the utility of engaging older adults across the continuum, illustrating effective strategies for establishing trusted academic-community partnerships. We invite attendees to consider the value of this heuristic framework to plan and design fit-for-purpose community-engaged research projects. Community Engaged Research Interest Group Sponsored Symposium
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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 | 0.091 | 0.094 |
| 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 | 0.023 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.005 | 0.053 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".