Pedestrian injury and the built environment: an environmental scan of hotspots
Notice bibliographique
Résumé
BACKGROUND: Pedestrian injury frequently results in devastating and costly injuries and accounts for 11% of all road user fatalities. In the United States in 2006 there were 4,784 fatalities and 61,000 injuries from pedestrian injury, and in 2007 there were 4,654 fatalities and 70,000 injuries. In Canada, injury is the leading cause of death for those under 45 years of age and the fourth most common cause of death for all ages Traumatic pedestrian injury results in nearly 4000 hospitalizations in Canada annually. These injuries result from the interplay of modifiable environmental factors. The objective of this study was to determine links between the built environment and pedestrian injury hotspots in Vancouver. METHODS: Data were obtained from the Insurance Corporation of British Columbia (ICBC) for the 6 year period from 2000 to 2005 and combined with pedestrian injury data extracted from the British Columbia Trauma Registry (BCTR) for the same period. High incident locations (hotspots) for pedestrian injury in the City of Vancouver were identified and mapped using geographic information systems (GIS), and the characteristics of the built environment at each of the hotspot locations were examined by a team of researchers. RESULTS: The analysis highlighted 32 pedestrian injury hotspot locations in Vancouver. 31 of 32 hotspots were situated on major roads. Likewise, the majority of hotspots were located on downtown streets. The 'downtown eastside' was identified as an area with multiple high-incident locations, including the 2 highest ranked pedestrian injury hotspots. Bars were present at 21 of the hotspot locations, with 11 of these locations being judged to have high alcohol establishment density. CONCLUSION: This study highlighted the disproportionate burden of pedestrian injury centred on the downtown eastside area of Vancouver. The environmental scan revealed that important passive pedestrian safety countermeasures were only present at a minority of high-incident locations. More importantly, bars were highly associated with risk of pedestrian injury. This study is the basis for potential public health intervention by clearly indicating optimal locations for signalized pedestrian crosswalks.
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 | 0,001 | 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 ».