The Long-Term Relationship Between High-Intensity Volunteering and Physical Activity in Older African American Women
Bibliographic record
Abstract
OBJECTIVES: Experience Corps (EC) places older volunteers in public elementary schools in 20 cities across the country. The EC program in Baltimore is a health promotion intervention designed to improve the academic outcomes of children and increase older adult volunteer physical activity. We sought to determine if there were sustained increases in physical activity with participation in EC. METHODS: Seventy-one African American women volunteers in the Baltimore EC were compared with 150 African American women in the Women's Health and Aging Studies (WHAS) I and II; all were aged 65-86 years with comparable Social Economic Status, frailty, and self-reported health status. Using a regression model, we evaluated physical activity adjusting for a propensity score and time of follow-up over 3 years. RESULTS: EC volunteers reported a sustained increase in physical activity as compared with the comparison cohort. Baseline physical activity for individuals with a median propensity score was 420 kcal/wk for both groups. At 36 months, EC volunteers reported 670 kcal/week compared with 410 kcal/week in WHAS (p = .04). Discussion These findings suggest that high-intensity senior service programs that are designed as health promotion interventions could lead to sustained improvements in physical activity in high-risk older adults, while simultaneously addressing important 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".