Encouraging Dog Walking for Health Promotion and Disease Prevention
Bibliographic record
Abstract
Regular physical activity is associated with numerous health benefits, including the prevention of many chronic diseases and conditions or a reduction in their adverse effects. Intervention studies suggest that promoting dog walking among dog owners who do not routinely walk their dogs may be an effective strategy for increasing and maintaining regular physical activity. Strategies that emphasize the value of dog walking for both dogs and people, promote the context-dependent repetition of dog walking, enhance the social-interaction benefits, encourage family dog walking, and ensure availability of public space for dog walking may encourage increased dog walking. Research also supports organizing buddy systems via "loaner" dogs to facilitate informal walking by dog owners and non-dog owners. Given the number of homes that have dogs, strategies that promote dog walking could be effective at increasing physical activity levels among a significant proportion of the population. Maximizing the potential for dog walking to positively influence the health of individual people (and dogs) will only occur through implementing programs with broad population-level reach. Policies that facilitate dog walking at the community and population levels, such as "dogs allowed" places, off-leash zones, and dog-friendly built environments and parks, may contribute to greater physical activity through dog walking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".