MétaCan
Menu
← Retour à la cohorte
Enregistrement W6986836458

Reducing the burden of road traffic injuries and deaths in Ghana: an implementation science approach

2023· dissertation· en· W6986836458 sur OpenAlexaff

Notice bibliographique

RevueResearchWorks at the University of Washington (University of Washington) · 2023
Typedissertation
Langueen
DomaineMedicine
ThématiquePrenatal Screening and Diagnostics
Établissements canadiensCentre for Global Health Research
Organismes subventionnairesnon disponible
Mots-clésRoad trafficPoison controlWork (physics)Occupational safety and healthHuman factors and ergonomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This dissertation comprises four studies that aim to develop contextually relevant evidence on high-risk areas and factors for targeted road traffic injury prevention initiatives in Ghana. We use an implementation science approach by conducting research to understand areas and populations at greater risk that may benefit from evidence-based policies and interventions. The first three studies use a longstanding database maintained by the Building and Road Research Institute of each police-reported motor vehicle collision, minor injury, severe injury, and death. The last study involves in-depth interviews with 24 people in high-risk areas of major roads in Ghana. In Chapter 1, we provide background on road traffic injuries and deaths, our research objectives, and our approach. In Chapter 2, we identify spatial-specific and precise (i.e., 100-meter locations) clusters of road traffic injury severity on major roads in rural and urban areas of Ghana using the Getis-Ord Gi* statistic in ArcGIS Pro. We map 35,109 road traffic events from 2017 to 2020 on major roads and weigh outcomes based on severity using a well-known injury severity index. We observed 223 hot spots in rural areas (6.2% of the road network) and 142 injury severity hot spots in urban areas (4.7%) of the road network to target for injury control. We find critical patterns in both types of hot spots, including that urban hot spots are often in the peripheral area of cities where major roads intersect, whereas rural hot spots are sporadic. In Chapter 3, we analyze spatiotemporal trends in road traffic injury severity, minor injuries, severe injuries, and deaths from 2005 to 2020 using space-time data mining tools (i.e., a space-time cube and emerging hot spot analysis) in ArcGIS Pro. We find the injury severity index and minor injuries significantly decreasing over the period while severe injuries and deaths are on the rise. We identify three specific 2 kilometers (km) by 2 km urban areas and 29 2 km by 2 km rural areas, which are intensifying, defined as hot spots for 90% of the 16 years and with increasing injury severity index scores. In Chapter 4, we use five years of data (2016 to 2020) to assess and compare factors associated with injury severity among vulnerable road users (VRUs), defined as those without external protection (e.g., pedestrians, powered two-wheeler occupants), to non-vulnerable. We find some factors that consistently increase the odds of severe outcomes, including head-on collisions, at night with no lights or the lights off, and in rural areas or villages. We also explore and compare VRU subgroups (powered two-wheeler occupants, powered three-wheeler occupants, cyclists, and pedestrians) and find substantial heterogeneity in the factors associated with injury severity. Findings indicate that a targeted and road user-specific injury prevention approach is warranted. Targeted interventions for each sub-group could include interventions related to nighttime risks (e.g., lighting, enforcement to reduce excessive speeding) for powered two-wheelers, improved regulation for passengers of powered three-wheelers, and pedestrian-oriented infrastructure. Lastly, Chapter 5 explores road user perspectives on the magnitude, contributing factors, and potential solutions for road traffic injuries and deaths. We find that participants feel excluded from road safety design, planning, and implementation efforts, despite their complex understanding of the problem’s magnitude and contributing factors. An emerging theme was that socio-economic conditions affect increased risks related to road traffic collisions, injuries, and deaths. For example, participants noted that roadside sellers were at higher risk because of their precarious economic status, requiring them to spend substantial time on busy, high-speed roads. Participants describe key implementation issues related to existing interventions, such as a lack of enforcement at certain places or times and poorly designed and built speed calming measures. When prompted for potential solutions, many road users focused on road user behavior and the need for increased enforcement, reflecting a national and global emphasis on changing road user behavior. We call for greater community involvement in road safety to adapt interventions to meet local needs and for the government to acknowledge the gravity of this crisis and allocate resources accordingly.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,041
Score d'incertitude au seuil0,087

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,026
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

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.

Tête enseignante Opus0,028
Tête enseignante GPT0,303
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueResearchWorks at the University of Washington (University of Washington)→Même sujetPrenatal Screening and Diagnostics→Travaux en français237 207→