OLDER ADULTS AND THE WORLD CAFé APPROACH: CROSS-GENERATIONAL INITIATIVES IN RESEARCH AND EDUCATION
Bibliographic record
Abstract
Abstract In Canada, numbers of older adults are considered to be increasing, and by 2036, it is expected that seniors will reach 25% of the total population. Since 2009, the Saskatchewan Population Health and Evaluation Research Unit (SPHERU) has developed an interdisciplinary approach to a community-based research program focused on rural older adults. The world café approach is recognized as collaborative and ideal for encouraging dialogue, sharing knowledge, and developing action plans. Set up like a café, four to six participants at each table engage in a series of three conversational rounds lasting approximately 20 minutes each. At the end of each round, participants move to different tables while the facilitator(s) remain at their original tables. We incorporated a world café approach in three distinct research projects, facilitating a total of five world café events. For each of these events, we also engaged with graduate and undergraduate students who were trained to serve as table facilitators. Participating students represented a variety of disciplines including social work, nursing, and gerontology. Older adults participating in the world café events reported positive experiences and appreciation for the opportunity to discuss new information. Student facilitators identified their participation as a “real life” learning and networking opportunity that enhanced their classroom experiences. Challenges identified included issues related to individual mobility, and issues related to noise and sound quality for those with hearing deficiencies. A community-based approach to research is effective when engaging with this population, and a word café event brings seniors directly into the discussion.
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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 | 0.058 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".