Field validation of listings of food stores and commercial physical activity establishments from secondary data
Notice bibliographique
Résumé
BACKGROUND: Food- and activity-related establishments are increasingly viewed as neighbourhood resources that potentially condition health-related behaviour. The primary objective of the current study was to establish, using ground truthing (on-site verification), the validity of measures of availability of food stores and physical activity establishments that were obtained from commercial database and Internet searches. A secondary objective was to examine differences in validity results according to neighbourhood characteristics and commercial establishment categories. METHODS: Lists of food stores and physical activity-related establishments in 12 census tracts within the Montreal metropolitan region were compiled using a commercial database (n = 171 establishments) and Internet search engines (n = 123 establishments). Ground truthing through field observations was performed to assess the presence of listed establishments and identify those absent. Percentage agreement, sensitivity (proportion of establishments found in the field that were listed), and positive predictive value (proportion of listed establishments found in the field) were calculated and contrasted according to data sources, census tracts characteristics, and establishment categories. RESULTS: Agreement with field observations was good (0.73) for the commercial list, and moderate (0.60) for the Internet-based list. The commercial list was superior to the Internet-based list for correctly listing establishments present in the field (sensitivity), but slightly inferior in terms of the likelihood that a listed establishment was present in the field (positive predictive value). Agreement was higher for food stores than for activity-related establishments. CONCLUSION: Commercial data sources may provide a valid alternative to field observations and could prove a valuable tool in the evaluation of commercial environments relevant to eating behaviour. In contrast, this study did not find strong evidence in support of commercial and Internet data sources to represent neighbourhood opportunities for active lifestyle.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».