Feasibility Of A “Walk With A Future Doc” Program In A Rural Canadian Community
Bibliographic record
Abstract
Community walking programs provide an option for individuals to be physically active and improve their mood. Rural communities experience challenges in engaging in regular physical activity (PA). PURPOSE: Determine the feasibility of a student-doctor-led walking program in a rural setting and whether it can improve mood. METHODS: In 2022, 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 up to 12 weeks with rolling recruitment. Participants that attended at least six walks completed a satisfaction survey at the end of the program. The number of healthcare providers, students, and walkers were recorded each week, as well as the walking distance covered. Mood was measured before and after each walk as sad (-1), neutral (0), or happy (1). RESULTS: 45 participants attended at least one walk over the 12 weeks, with an average of 21 ± 4 participants/walk. Most participants (n = 24) attended at least six walks (67% female, age range 61-70 years, average number of health conditions 2.3 ± 1.8). Six participants attended once and seven were lost to follow-up. Seven medical students and three physicians joined the program, with an average of two students and two physicians/walk. The average participant walking distance was 3.5 ± 0.8 km/session. Participants reported the primary reasons motivating them to join and continue the program were enjoyment of PA and belief in its importance (79% of participants), accessibility of the program (75%), meeting medical students (71%), health education talks (71%), and it being free (71%). Average mood increased following each walk (0.62 ± 0.31 to 0.94 ± 0.09, p < 0.001). When participants reported a bad pre-walk mood, their mood was good at the end of the session in 6/8 instances. When participants reported a neutral pre-walk mood, it improved by the end of the session in 61/67 instances. CONCLUSIONS: Our program attracted a reasonable number of individuals, promoted PA in a rural community, and acutely improved mood. The primary motivators for joining and the feasibility outcomes offer direction for upscaling this program and providing more options for PA in rural communities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".