Increasing self-efficacy and meeting the physical activity needs of community older adults through a community-based learning project
Bibliographic record
Abstract
Community-based learning (CBL) best serves students and community members when organizers reflect on the needs, concerns, and intentions of society. Worldwide, an area of focus within communities is their rapid expansion of older adults (OA) and a general increased population age. Within the U.S. it is estimated that a quarter of the population will be 65 or older by 2060. This concern is mirrored in the goals of government and professional organizations. For instance, some goals of Healthy People 2030 are to reduce the risk of diabetes, osteoporosis and fall-related injuries in OA. Increasing physical activity (PA) among OA is a key strategy to prevent chronic disease, sudden fall injuries and improve quality of life. A central learning outcome of health science and exercise science (HES) undergraduate curricula is teaching students how to help individuals combat sedentary lifestyles by increasing their PA. CBL is recognized as a pedagogical method for students to engage with the community and practice hands-on learning. This article describes how a 10-year CBL partnership between HES faculty and a community organization serving OA was developed for an undergraduate HES program and evolved to support curricular and community needs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".