The evaluation of product and process for in-flight decision-making training

Forty-One male pilots from ROC Air Force Tactical Training Wings participated in the study. The flying experience of participants was between 354 and 220 hours with an average of 292 hours. Participants were randomly divided into two groups, 21 pilots in the experimental group, and 20 pilots in control group. Two ADM mnemonic methods, SHOR and DESIDE, that had been previously been assessed by instructor pilots as being the most applicable and having the potential to significantly improve the quality of military pilots’ decision-making formed the basis of the ADM training programs. Overall, results from both the simulator-based trials (which assessed the product of the ADM training programme) and the pencil-and-paper tests (which assessed the process that the trainees applied) showed gains being made in both Situation Assessment and Risk Management skills attributable to the decision making training course. The results strongly suggest that such a short training course can be effective in terms of improving pilots’ skill in situation assessment and risk management. However, these gains were at the cost of a decreased speed of responding. Nevertheless, it is suggested that a simple, short, cost-effective training program in the appropriate use of ADM mnemonic methods may ultimately produce significant gains in flight safety. Such a course may easily be integrated into current CRM or simulator-based training programs

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

Human Factors Analysis and Classification System (HFACS) is based upon Reason’s organizational model of human error which suggests that there is a ‘one to many’ mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations