title: The Health Belief Model variables as predictors of risky driving behaviors among commuters in Yazd, Iran.
abstract: BACKGROUND
  Road traffic injuries are a major but neglected global public health problem. The human factor appears in the literature as the most prevalent contributing factor of road traffic crashes. The purpose of the study was to utilize components of the Health Belief Model to predict risky driving behaviors among a sample of commuters in Yazd, Iran.
  
  
  METHODS
  A cross-sectional, correlational design was employed. A two-stage cluster sampling was used to recruit 300 drivers to participate in the study.
  
  
  RESULTS
  The most reported risky driving behavior was speaking with others and using a cell phone, both while driving. There was a negative statistically significant association between risky driving behaviors and age. The occurrence of risky driving behaviors was higher among males as well as single drivers and was inversely related to education level. There was also a positive statistically significant correlation between risky driving behaviors and road traffic accidents and the number of traffic citations/fines. Perceived severity, susceptibility, barriers, threat, and net benefits were significantly related to risky driving behaviors.
  
  
  CONCLUSIONS
  The pattern of risky driving behaviors, and safe driving barriers among commuters in Yazd, Iran, which are identified in this study, could be used in planning effective intervention programs to improve the driving habits of the commuters. The results of the study showed that Health Belief Model can be used as a conceptual framework for intervention programs aimed at decreasing road traffic accidents.
authors.affiliation: School of Health, Yazd Shahid Sadooghi University of Medical Sciences, Yazd, Iran. morowaty@yahoo.com
authors.forename: Mohammad Ali
authors.initials: MA
authors.lastname: Morowatisharifabad
mesh_terms: D000063:Accidents, Traffic / Q000523:psychology / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D001334:Automobile Driving / Q000331:legislation & jurisprudence / Q000523:psychology* / Q000706:statistics & numerical data; D040421:Cell Phone; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D007492:Iran; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D012044:Regression Analysis; D012307:Risk Factors; D012309:Risk-Taking; D012737:Sex Factors; D012959:Socioeconomic Factors; D014186:Transportation; D055815:Young Adult