Development, scoring, and reliability of the Microscale Audit of Pedestrian Streetscapes (MAPS)
Notice bibliographique
Résumé
BACKGROUND: Streetscape (microscale) features of the built environment can influence people's perceptions of their neighborhoods' suitability for physical activity. Many microscale audit tools have been developed, but few have published systematic scoring methods. We present the development, scoring, and reliability of the Microscale Audit of Pedestrian Streetscapes (MAPS) tool and its theoretically-based subscales. METHODS: MAPS was based on prior instruments and was developed to assess details of streetscapes considered relevant for physical activity. MAPS sections (route, segments, crossings, and cul-de-sacs) were scored by two independent raters for reliability analyses. There were 290 route pairs, 516 segment pairs, 319 crossing pairs, and 53 cul-de-sac pairs in the reliability sample. Individual inter-rater item reliability analyses were computed using Kappa, intra-class correlation coefficient (ICC), and percent agreement. A conceptual framework for subscale creation was developed using theory, expert consensus, and policy relevance. Items were grouped into subscales, and subscales were analyzed for inter-rater reliability at tiered levels of aggregation. RESULTS: There were 160 items included in the subscales (out of 201 items total). Of those included in the subscales, 80 items (50.0%) had good/excellent reliability, 41 items (25.6%) had moderate reliability, and 18 items (11.3%) had low reliability, with limited variability in the remaining 21 items (13.1%). Seventeen of the 20 route section subscales, valence (positive/negative) scores, and overall scores (85.0%) demonstrated good/excellent reliability and 3 demonstrated moderate reliability. Of the 16 segment subscales, valence scores, and overall scores, 12 (75.0%) demonstrated good/excellent reliability, three demonstrated moderate reliability, and one demonstrated poor reliability. Of the 8 crossing subscales, valence scores, and overall scores, 6 (75.0%) demonstrated good/excellent reliability, and 2 demonstrated moderate reliability. The cul-de-sac subscale demonstrated good/excellent reliability. CONCLUSIONS: MAPS items and subscales predominantly demonstrated moderate to excellent reliability. The subscales and scoring system represent a theoretically based framework for using these complex microscale data and may be applicable to other similar instruments.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».