Examining the Relationship between Park Neighborhoods, Features, Cleanliness, and Condition with Observed Weekday Park Usage and Physical Activity: A Case Study
Bibliographic record
Abstract
Background: Little research has comprehensively explored how park features, quality indicators, and neighborhood environments are associated with observed park usage and physical activity (PA). This case study examined whether weekday park usage and PA differ by neighborhood type, across numerous categories of park features, and according to park feature condition and cleanliness. Methods: Direct observation was used to capture the number of users and PA levels within 143 park features in 6 parks (3 urban, 3 suburban) over the course of six weeks. Audits of park environments assessed the type, condition, and cleanliness of all features and amenities. Results: Urban parks experienced greater usage, but a higher proportion of sedentary users than suburban parks. Usage and PA levels differed across types of park features, with splash pads, pools, paths, and play structures having the greatest proportion of active users. Usage did not differ by park feature condition and cleanliness, but greater condition and cleanliness were generally associated with higher PA levels. Conclusions: Factors such as neighborhood context, types of park features, and condition and cleanliness can impact park usage and PA levels and should be targets for researchers and planners aiming to foster more user-friendly and active neighborhood park environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".