Translating Knowledge
Bibliographic record
Abstract
INTRODUCTION: Intergenerational programs have been touted to address the generation gaps and isolation of older adults. Mutual contact alone has produced mixed results, but attention to the intergenerational program content demonstrates well-being benefits. This practice-based article examines the benefits of creating and performing ensemble-created plays to older adults' and university students' well-being and the key processes that promote well-being. METHOD: This community participatory research project involved older adults as researchers as well as research subjects. Individual semistructured interviews were conducted by two trained interviewers with older adults (n = 15) and university students (n = 17). RESULTS: Professional dramaturgical processes of storytelling, reminiscence, and playfulness were key elements in participants' generative learning. They augmented older adults' and university students' ability to understand their situations and try innovative solutions. Skills such as openness, flexibility, and adaptation transferred into students' and older adults' daily lives. CONCLUSION: Participating in this intergenerational theatre group reduced ageism and improved intergenerational relationships. It increased older adults' and university students' well-being by building social networks, confidence, and self-esteem and developed a sense of social justice, empathy, and support for others.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.375 | 0.243 |
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".