Online Newspaper Reports on Ambulance Accidents in Austria, Germany, and Switzerland: Retrospective Cross-sectional Review
Notice bibliographique
Résumé
BACKGROUND: Ambulance accidents are an unfortunate indirect result of ambulance emergency calls, which create hazardous environments for personnel, patients, and bystanders. However, in European German-speaking countries, factors contributing to ambulance accidents have not been optimally researched and analyzed. OBJECTIVE: The objective of this study was to extract, analyze, and compare data from online newspaper articles on ambulance accidents for Austria, Germany, and Switzerland. We hope to highlight future strategies to offset the deficit in research data and official registers for prevention of ambulance and emergency vehicle accidents. METHODS: Ambulance accident data were collected from Austrian, German, and Swiss free web-based daily newspapers, as listed in Wikipedia, for the period between January 2014 and January 2019. All included newspapers were searched for articles reporting ambulance accidents using German terms representing "ambulance" and "ambulance accident." Characteristics of the accidents were compiled and analyzed. Only ground ambulance accidents were covered. RESULTS: In Germany, a total of 597 ambulance accidents were recorded, corresponding to 0.719 (95% CI 0.663-0.779) per 100,000 inhabitants; 453 of these accidents left 1170 people injured, corresponding to 1.409 (95% CI 1.330-1.492) per 100,000 inhabitants, and 28 of these accidents caused 31 fatalities, corresponding to 0.037 (95% CI 0.025-0.053) per 100,000 inhabitants. In Austria, a total of 62 ambulance accidents were recorded, corresponding to 0.698 (95% CI 0.535-0.894) per 100,000 inhabitants; 47 of these accidents left 115 people injured, corresponding to 1.294 (95% CI 1.068-1.553) per 100,000 inhabitants, and 6 of these accidents caused 7 fatalities, corresponding to 0.079 (95% CI 0.032-0.162) per 100,000 inhabitants. In Switzerland, a total of 25 ambulance accidents were recorded, corresponding to 0.293 (95% CI 0.189-0.432) per 100,000 inhabitants; 11 of these accidents left 18 people injured, corresponding to 0.211(95% CI 0.113-0.308) per 100,000 inhabitants. There were no fatalities. In each of the three countries, the majority of the accidents involved another car (77%-81%). In Germany and Switzerland, most accidents occurred at an intersection. In Germany, Austria, and Switzerland, 38.7%, 26%, and 4%, respectively, of ambulance accidents occurred at intersections for which the ambulance had a red light (P<.001). In all three countries, most of the casualties were staff and not uncommonly a third party. Most accidents took place on weekdays and during the daytime. Ambulance accidents were evenly distributed across the four seasons. The direction of travel was reported in 28%-37% of the accidents and the patient was in the ambulance approximately 50% of the time in all countries. The cause of the ambulance accidents was reported to be the ambulance itself in 125 (48.1% of accidents where the cause was reported), 22 (42%), and 8 (40%) accidents in Germany, Austria, and Switzerland, respectively (P=.02), and another vehicle in 118 (45.4%), 29 (56%), and 9 (45%) accidents, respectively (P<.001). A total of 292 accidents occurred while blue lights and sirens were used, which caused 3 deaths and 577 injuries. CONCLUSIONS: This study draws attention to much needed auxiliary sources of data that may allow for creation of a contemporary registry of all ambulance accidents in Austria, Germany, and Switzerland. To improve risk management and set European standards, it should be mandatory to collect standardized goal-directed and representative information using various sources (including the wide range presented by the press and social media), which should then be made available for audit, analysis, and research.
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 | 0,005 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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 ».