Changes in visitor profiles and activity patterns following dog supportive modifications to parks: A natural experiment on the health impact of an urban policy
Bibliographic record
Abstract
Urban parks are important settings for physical activity, but few natural experiments have investigated the influences of park modifications on activity patterns and visitor profiles.We assessed the impact of implementing a municipal policy on off-leash dogs in city parks in Calgary (Alberta, Canada). Systematic observation undertaken in 2011 and 2012 within four parks captured patterns of use, activities, and visitors׳ characteristics. After baseline data collection, off-leash areas were created in two parks only. We compared changes in the sociodemographic and activity profiles in all parks between 2011 and 2012. Visitors with dogs participated in less intense activity relative to visitors without dogs. In both modified parks, the intensity of children׳s activities decreased, while the intensity of adults’ activities remained stable. Adjusting for visitor characteristics, the likelihood of dog-related visits, relative to other activities, significantly decreased in one of the two modified parks (odds ratio 0.55, p <.05). Accommodating off-leash dogs in parks has the potential to modify activities undertaken inside parks as well as the profile of visitors, but may not increase park visits among dog-walkers in the short term. Recreation, park, and urban planners and policy-makers need to consider the needs and preferences of the broader community in the design and redesign of public parks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".