Silver Sneakers in Central PA: Assessment of a Community based Exercise Program in a Mixed Rural/Urban Catchment Area
Bibliographic record
Abstract
Abstract Older cancer survivors present with unique challenges that may impact quality of life and increase physical dysfunction if not properly managed. Regular physical activity (PA) can help mitigate these effects. Silver Sneakers (SS), a free exercise program available to Medicare beneficiaries, has more than 16,000 US locations. To understand capacity of SS to serve older adults in our mixed rural/urban catchment area of Central Pennsylvania, we 1) identified all registered SS program locations in our 28-county catchment area and; 2) conducted phone questionnaires with SS program staff. Approximately 18 gyms closed during the pandemic, leaving a sample of 121 participating gyms. We talked to 80 gyms (66% response rate) to understand member and programming characteristics, training of staff and program marketing. Geographic locations of SS were mixed – 39% in rural and 61% in urban counties; the majority (43%) were located in private gyms or YMCAs. The majority of gyms reported membership was equally mixed by gender and described ages of members as 65-80 years (94%). Program staff said that many members exercised several times per week with friends/family. Program staff also reported that social opportunities (35%) were a primary reason participants remained active in SS. Most (89%) of the facilities were still able to offer SS during the pandemic, with the majority (60%) adapting format to Zoom and other video platforms to conduct classes. Overall, SS programs offer a sustainable option to facilitate access to exercise programs and reduce barriers to PA among older adults in our catchment area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".