Social Tie Benefits Framework for Older Adult Group Physical Activity
Bibliographic record
Abstract
We developed a framework of social benefits of participating in group physical activities for older adults to aid practitioners in translating concepts related to social benefits into the design and evaluation of group physical activities, and challenges to promoting social benefits. Using interpretive description, we developed a draft conceptual framework, which was further developed empirically using focus group data from 18 staff in roles related to promoting social benefits for older adults through group physical activities in a municipality with an Age-Friendly Cities strategy, and informed by interviews with 2 older adults with group physical activity experience. The framework delineated five categories of social benefits (role models, social networks, social participation, social connection, and social support) on a continuum of social tie strength. Challenges related to funding, evaluation, barriers to access, and limitations in knowledge and training. The framework has potential for building understanding of social benefits among practitioners across disciplines, and guiding practitioners to identify relevant social benefits for particular physical activity classes and align them with strategies and evaluation tools. It also has implications for research including developing techniques for training staff to promote social benefits, and for policy such as reducing silos in professional roles and funding.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".