Socioeconomic and race/ethnic disparities in observed park quality
Notice bibliographique
Résumé
BACKGROUND: Though park presence and access disparities are well studied for their associations with physical activity (PA), disparities in the availability and quality of amenities and facilities within parks have been infrequently examined. METHODS: Five hundred forty-three parks from 472 block groups in the Seattle, WA and Baltimore, MD regions were audited using the Environmental Assessment of Public Recreation Spaces (EAPRS) to assess presence and quality (e.g., condition, cleanliness) of amenities (e.g., restrooms, seating) and facilities (e.g., fields, courts). General linear model regressions investigated Census 2000-derived neighborhood race/ethnicity and income main effect and interactive relationships with 7 park quality summary scores: 1) trails, 2) open space, 3) sports facilities, 4) PA facilities count, 5) PA facilities quality, 6) aesthetics, and 7) overall amenities, controlling for park size. The regions were analyzed separately due to differing race/ethnicity distributions. RESULTS: In the Seattle region, neighborhood income was significantly negatively associated with sports quality score (p < .043), PA facilities total count (p < .015) and the overall amenities quality score (p < .004) (unexpected direction). In the Baltimore region, neighborhood race/ethnicity (percent White/non-Hispanic) was significantly positively related to the open spaces quality score (p < .011) (expected direction). A significant income-by-race/ethnicity interaction was found for PA facilities quality (p = .014), with high-percent minority neighborhoods having higher quality parks in high- vs. low-income neighborhoods, yet was opposite in mostly White/non-Hispanic neighborhoods. The other income-by-race/ethnicity interaction was for overall amenities quality score (p = .043), where scores in high-percent minority neighborhoods were best in high- vs. low-income neighborhoods. There was little difference in scores within mostly White or mixed neighborhoods by income. CONCLUSIONS: Patterns of association of neighborhood race/ethnicity and income with park qualities differed between regions. In the Seattle region, "equitable differences" were found, where lower income neighborhoods had better park quality on average. In the Baltimore region, park quality was more consistently negatively associated with income and race/ethnic diversity, and complex interactions of race/ethnicity by income were detected. These findings emphasize the need to explore other factors that may explain variations in park quality, like local policy, citizen involvement in park decision-making, park funding and allocation, sources of funding and park priorities.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».