Are park proximity and park features related to park use and park-based physical activity among adults? Variations by multiple socio-demographic characteristics
Bibliographic record
Abstract
BACKGROUND: Parks are valuable resources for physical activity (PA) given their widespread availability and low cost to maintain and use. Both proximity to parks and the availability of particular features are important correlates of PA. However, few studies have explored multiple measures of proximity simultaneously or the specific facilities associated with park use and park-based PA among adults, let alone differences across socio-demographic characteristics. The purpose of this study was to examine associations between park proximity and park facilities and adults' park use and park-based PA, while also exploring differences by gender, age, race, and income. METHODS: Data on monthly park use and weekly amount of PA undertaken in parks were collected via a mail survey of adults from randomly-selected households (n = 893) in Kansas City, Missouri (KCMO) in 2010-2011. Three measures of park proximity were calculated within 1 mile of participating households: distance to the closest park, number of parks, and total park area. All parks in KCMO were audited using the Community Park Audit Tool to determine the availability of 14 park facilities within 1 mile of each participant (e.g., trail, playground, tennis court). Multilevel logistic regression was used to examine the relationship between each of park use and park-based PA and 1) three measures of park proximity, and 2) the availability of 14 park facilities within 1 mile of participants. Separate analyses were conducted by gender, age, race, and income, while controlling for all socio-demographic characteristics and BMI. RESULTS: Across all sub-samples, distance to the closest park was not significantly related to either park use or park-based PA. However, numerous significant associations were found for the relationship of number of parks and amount of park space within 1 mile with both outcomes. As well, diverse facilities were associated with park use and park-based PA. For both park proximity and facilities, the significant relationships varied widely across gender, age, race, and income groups. CONCLUSIONS: Both park proximity and park facilities are related to park use and park-based PA. Understanding how such associations vary across demographic groups is important in planning for activity-friendly parks that are responsive to the needs of neighborhood residents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".