Improving Senior Fitness Programs & Dementia Care (Canadian Centre for Activity & Aging)
Bibliographic record
Abstract
Our team worked alongside the Canadian Centre for Activity and Aging (CCAA) to improve senior fitness programs and dementia care through volunteering at weekly exercise classes, assisting with fitness assessments, and creating two tangible deliverables. Our first deliverable was a Wordle; a tool for visualizing the modifiable risk factors of dementia. By consulting existing literature, we concluded that hearing loss, low education status, depression, and smoking were the main modifiable risk factors. This Wordle will be used for future research and educational purposes. Our next deliverables targeted the fitness aspect of the CCAA. We helped facilitate weekly instructor-led fitness classes and recorded our observations each time. We also conducted a functional fitness assessment to obtain baseline measurements of each participant’s functional abilities. Future measurements can then be compared with these values to evaluate the fitness classes’ efficacy at reducing or ameliorating declines in physical functioning. Some assessments required more time to complete than others, which reduced testing efficiency. Participants had the most difficulty with the timed up-and-go, 30-second arm curl, 30-second chair stand, and 2-minute step tests. Male participants were less likely to meet established standards compared with their female counterparts. Measurements were recorded using the Healthy Active Living Database (HAroLD) which was straightforward but difficult to use in real-time. Observations and recommendations were summarized with an infographic that will inform the CCAA’s management team about our contributions this term. Future students working with the CCAA can use our deliverables to improve the curriculum.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".