Impact Of A Walk With A Future Doc Program On Participant Frailty Levels
Bibliographic record
Abstract
Frailty reflects the accumulation of health deficits across multiple health domains (e.g., mobility, cognition, vision, etc.) that increases the vulnerability to further adverse health outcomes, including morbidity and mortality. Community walking programs provide an opportunity for individuals to be physically active and may improve frailty levels. PURPOSE: Determine the impact of a student-doctor-led walking program on participant frailty. METHODS: From 2022-2023, we implemented a Walk with a Future Doc program that comprised a weekly walk and education session led by medical students. The group met for one hour/week for to 12 weeks. All participants completed the Canadian Longitudinal Study on Aging Frailty Index (FI) questionnaire before and after the program. The FI included 65 self-reported health items that covered basic activities of daily living, instrumental activities of daily living, mobility, mood, and chronic health conditions. FI was determined as the number of deficits present ÷ deficits measured. We ran a two-way group (Non-Frail: FI <0.10 vs. Very-Mild+ Frail: FI ≥ 0.10) repeated measures analysis of variance with Bonferroni post-hoc testing to compare pre- and post-frailty indexes. RESULTS: Seventy-two participants completed the program and were primarily aged 61-70 years and female (~67%). Prior to starting the program, 49/72 were non-frail (FI <0.10) and 23/72 had a FI ≥0.10. We observed a group βψ time interaction (p = 0.01), with the Non-Frail group being unchanged (0.053 ± 0.029 to 0.059 ± 0.029; p = 0.14), but the Very-Mild+ Frail group reducing their frailty level from 0.169 ± 0.079 to 0.156 ± 0.071 (p = 0.02). CONCLUSIONS: A community-based walking program incorporating education and medical students was effective for reducing frailty levels, particularly among older adults with FIs ≥0.10, supporting the effectiveness of movement for improving the health of people with health deficits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".