Creative Arts and Well Being in Seniors Living in a Retirement Community
Bibliographic record
Abstract
In 2011, 5.0 million Canadians were 65 years and older and Arts Health Network Canada predicts that by 2036 that number will soar to 10.4 million. This enormous surge will increase the need for public and private assisted living for seniors. Now is the time to look at opportunities for our ageing population. Our research hopes to contribute to the reshaping of creative arts engagement and ageing. Since 2014, the National Center for Creative Ageing (NCCA) has held an annual conference on how the arts help older adults lead healthier lives. There has been in recent years a necessity for such an exchange between researchers, psychologists, medical doctors, educators, therapists, stakeholders, etc. Our research question is: How does drama impact the sense of well-being of seniors living in retirement homes? As a starting place, we will look to the research of Dr. Gene D. Cohen, a psychiatrist and pioneer of geriatric mental health whose longitudinal study, the first of its kind, provides proof of the positive impact of cultural programs on the health and well-being of older adults. This is relevant to our research because we are also hoping to record the affects our program will have on older adults well being. We will be using professor of music education, Dr. Andrea Creech's three factors well-being; purpose, autonomy and social affirmation. In February and March, our researchers will facilitate weekly workshops at Amica at Windsor Retirement Living with a group of participants. To measure the impact of drama on their sense of well-being of seniors living in retirement homes, we will use two primary methodologies: ongoing reflective praxis and post interviews.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".