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Occupational Cognitive Failures and Safety Performance in the Workplace

Allahyari T., Rangi N. H., Khalkhali H., Khosravi Y.

International Journal of Occupational Safety and Ergonomics

2014

Vol. 20, No. 1

175--180

Introduction. The majority of industrial accidents occur because of human errors. Human error has different causes, however, in all cases cognitive abilities and limitations of human play an important role. Occupational cognitive failures are cognitively-based human errors that occur at work. The aim of this study was to examine the relationship between occupational cognitive failures and safety consequences. Method. Personnel of a large industrial company in Iran filled out an occupational cognitive failure questionnaire (OCFQ) and answered questions on accidents. Univariate and multiple logistic regression analysis were used to determine the relationship between cognitive failures and safety consequences. Results. According to developed regression models, personnel with a high rate of cognitive failure, in comparison to low rate, have a high risk of minor injury involvement (OR 5.1, 95% CI [2.62, 10.3]); similar results were for major injury and near miss. Discussion. The results of this study revealed usefulness of the OCFQ as a tool of predicting safetyrelated consequences and planning preventive actions.

Cognitive Failures, Driving Errors and Driving Accidents

Allahyari T., Saraji G. N., Adl J., Hosseini M., Iravani M., Younesian M., Kass S. J.

International Journal of Occupational Safety and Ergonomics

2008

Vol. 14, No. 2

149--158

Introduction. The impact of a driver’s cognitive capability on traffic safety has not been adequately studied. This study examined the relationship between cognitive failures, driving errors and accident data. Method. Professional drivers from Iran (160 males, ages 18–65) participated in this study. The cognitive failures questionnaire (CFQ) and the driver error questionnaire were administered. The participants were also asked other questions about personal driving information. A principal component analysis with varimax rotation was performed to determine the factor structure of the CFQ. Poisson regression models were developed to predict driving errors and accidents from total CFQ scores and the extracted factors. Results. Total CFQ scores were associated with driving error rates, but not with accidents. However, the 2 extracted factors suggested an increased effect on accidents and were strongly associated with driving errors. Discussion. Although the CFQ was not able to predict driving accidents, it could be used to identify drivers susceptible to driving errors. Further development of a driving-oriented cognitive failure scale is recommended to help identify error prone drivers. Such a scale may be beneficial to licensing authorities or for developing driver selection and training procedures for organizations.