BRAIN HEALTH IN COMMUNITY: INVOLVING AND COLLABORATING WITH COMMUNITIES YIELDED UNEXPECTED INNOVATIONS
Bibliographic record
Abstract
Abstract Funded by university-community engagement initiatives, this research aimed to understand and meet gaps in cognitive health promotion in diverse communities. The research team started with CONSULTATIVE conversations with activity providers and partnered older adults for analytic model specification and interpretation. As the focus groups progressed, the engagement fluidly progressed into an INVOLVEMENT marked by mutual respect for the distinct expertise of both parties and quick successions of knowledge exchanges, which led to two unintended innovations. Due to the trust built with key informants, older adults openly shared their unmet needs for a space to find meaning amid the challenges of aging. In response, a novel mindful discussion program was COLLABORATIVELY piloted with community organizations. Moreover, trust from older adults begot trust from activity providers and their sharing led to a simple eMental Health solution for closed-loop social prescribing. These insights on trust, engagement levels, and innovations are gained only in retrospect. Referencing these experiences, we reflect on the importance trust built with older adults as a catalyst for deeper engagements with service providers. Low-intensity consultations were more manageable at the start of the researcher-community relationship. More extensive engagements with older adults led to greater trust and mutual empowerment, and incidentally provided access to implicit knowledge held by activity providers which was crucial for innovation. Pivotal moments of knowledge spillovers were part of everyday activities in the community as researchers became embedded in relationality. Our experience underscores fluid engagement approaches that defy our best-planned intentions as lessons in decolonial knowledge cultivation.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".