Human Factors Methods and Accident Analysis by Paul M. Salmon et al.

By Paul M. Salmon et al.

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Sequential models, characterised by Heinrich舗s (1931) domino theory accident causation model, view accidents simply as the result of a sequence of linear events, with the last event being the accident itself. Epidemiological models, characterised by Reason舗s 舖Swiss cheese舗 model (although Reason himself disagrees with this, Reason 2008), view accidents much like the spreading of disease (Hollnagel 2004), and describe the combination of latent conditions present in the system for some time and their role in unsafe acts made by operators at the so-called 舖sharp end舗 (Reason 1997; Hollnagel 2004).

Although different in many ways, the most prominent models of accident causation are in agreement on at least one thing; that is, in order to exhaustively describe accidents, the entire complex sociotechnical system should be the unit of the analysis. 1 INTRODUCTION TO HUMAN FACTORS METHODS Many Human Factors methods exist. For the purposes of this book, these can be categorised as follows: 1. Data collection methods. The starting point in any Human Factors analysis, be it for accident analysis, system design or evaluation, involves describing existing or analogous systems via the application of data collection methods (Diaper and Stanton 2004).

Following this, the interviewer uses the cognitive probes in a semi-structured interview format in order to elicit information regarding the cognitive processes employed by the decision maker during the task, incident phase or decision point. It is important to note that the interviewer does not limit themselves to the CDM probes; rather, the approach is flexible in that further open, closed, and probing questions can be asked depending on the analysis requirements. An interview transcript should be recorded by the interviewer and the interview should be recorded using video and/or audio recording equipment.

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