Relationship of Field Dependence and Motor-Vehicle-Accident Involvement

1970 ◽  
Vol 31 (1) ◽  
pp. 272-274 ◽  
Author(s):  
Richard M. Harano
Keyword(s):  
Field Dependence ◽  
Traffic Safety ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Motor Vehicles ◽  
Future Research ◽  
California Department ◽  
Research Survey ◽  
Vehicle Accident ◽  
Relationship Of

A group of 28 accident and 27 accident-free drivers were requested by the California Department of Motor Vehicles to participate in a Driver Research Survey, the purpose being to evaluate the relationship between field dependence and motor-vehicle-accident involvement. The multiple regression results indicated that field dependence was significantly related to accident involvement. The results suggest that measures of perceptual style such as field dependence may hold promise for future research in traffic safety.

Virtual reality and computer games in the treatment of driving phobia induced by a motor vehicle accident

2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim
Keyword(s):  
Virtual Reality ◽  
Computer Games ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

Aspirated teeth - rigid or flexible bronchoscopy

2019 ◽  
pp. 1-2
Keyword(s):  
Clinical Practice ◽  
Foreign Bodies ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Flexible Bronchoscopy ◽  
Vehicle Accident

Tracheobronchial foreign bodies are a common problem in clinical practice. We present the case of a patient with three aspirated teeth following a motor vehicle accident.

Isolated second trimester uterine rupture following a motor vehicle accident

2021 ◽  
Vol 258 ◽  
pp. 461-463
Author(s):  
Tal Margaliot Kalifa ◽  
Misgav Rottenstreich ◽  
Eyal Mazaki ◽  
Hen Y. Sela ◽  
Schwartz Alon ◽  
...  
Keyword(s):  
Uterine Rupture ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Second Trimester ◽  
Vehicle Accident

scholarly journals Obstructive Sleep Apnea and Risk of Motor Vehicle Accident

2021 ◽  
Author(s):  
Gaia S. Pocobelli ◽  
Mary A. Akosile ◽  
Ryan N. Hansen ◽  
Joanna Eavey ◽  
Robert D. Wellman ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident ◽  
Obstructive Sleep
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