A community-based, medical student-led walking and education program was associated with a reduction in frailty levels among adults with elevated frailty
Bibliographic record
Abstract
Objectives: Frailty reflects the accumulation of health deficits that an individual develops over their lifespan. Walk with a Future Doc (WWAFD) is a medical student-led, community-based education and walking program. We examine the impact of a 12-week WWAFD program on lowering the frailty levels in a New Brunswick community and if the effects of the program would be specific to those with higher pre-WWAFD frailty levels. Methods: Eighty participants (age: 41-85 years; 51 female individuals) were recruited from the YMCA in New Brunswick (Canada) via word-of-mouth, social media, and consulting local physicians. The inclusion criteria were broad. All community members were welcome to attend the program, but only those over age 18 and those that attended ≥6/12 walks were included in the study. Participants were grouped into non-frail (FI< 0.10; n = 51) and very mild + frail (FI ≥ 0.10; n = 29) groups for comparison. Participants attended a student-led 1-h/week health education and walking program and completed the Canadian Longitudinal Study on Aging Frailty Index (FI) questionnaire before and after the program. Results: = 0.014). Conclusion and implications: The WWAFD program that included weekly walking and education sessions was associated with reduced frailty levels among adults with FI ≥0.10. This change emphasizes the value of community-based physical activity programs and exemplifies the impact they can have on the participants' health outcomes.
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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 | 0.000 | 0.001 |
| 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".