Unleashing Physical Activity: An Observational Study of Park Use, Dog Walking, and Physical Activity
Bibliographic record
Abstract
BACKGROUND: Walking has been identified as a low resourced yet effective means of achieving physical activity levels required for optimal health. From studies conducted around the world, we know that dog owners walk more than nondog owners. However, this evidence is largely self-reported which may not accurately reflect dog-owners' behaviors. METHOD: To address this concern, we systematically observed the use of 6 different public parks in Victoria, British Columbia during fair and inclement weather. Using a modified version of the SOPARC tool, we documented visitors' types of physical activity, and the presence or absence of dogs. The Physical Activity Resource Assessment was used to consider park features, amenities, and incivilities. RESULTS: More people without dogs (73%) visited the parks than those with dogs (27%), largely because of attendance at the multiuse sport parks during the summer months. Despite the opportunities to engage in multiple sports, most people used the parks to walk. However, when inclement weather struck, dog owners continued visiting parks and sustained their walking practices significantly more than nondog owners. CONCLUSION: Our observational snapshot of park use supports earlier work that dogs serve as a motivational support for their owners' walking practices through fair and foul weather.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".