Abstract
First, Paul Green and colleagues have provided the service of reviewing a large number of smartphone tools that people working in human factors may find useful. As you’ll see, they conclude that there are good apps for measuring sound: tools for performing frequency analyses ands tools for measuring ambient sound levels. They also describe apps for measuring length via clever ways of measuring distances greater than the screen dimensions. And they list numerous apps for various specialized forms of data collection and calculation. A number of these should prove useful in performing human factors work. Unfortunately, they report that the apps for measuring light levels are not, at present, adequate for professional work, although the authors offer hope that we will have useful light-meter apps in the relatively near future.
We then have an interesting article from Italy. Di Nocera and colleagues raise the question of whether it is possible to find evidence for the commonsense notion that accidents are sometimes caused by absentmindedness; that is, by the failure to pay attention, or what they call a “lack of active attentional control.” I have to confess to some skepticism regarding this question as well as their methodology, but we thought it important to publish this article because of its thought-provoking nature.
My skepticism stems from two problems that I see with attributing accidents to the failure to “pay attention.” First, it strikes me that the causal reasoning is often circular, in that the only evidence for poor attention is the very thing that the poor attention purports to explain: the accident itself. The other problem is that by stopping the causal chain at “attention,” we risk the flawed “operator-error” model that is generally a barrier to coming up with a practical solution.
However, Di Nocera et al. have provided some empirical evidence that the phenomenon does cause accidents. They asked tractor users to describe accident histories with tractors and also to take the Cognitive Failures Questionnaire (CFQ), which the authors used as a measure of the tendency to not pay attention. They found some tendency for accident frequency and high CFQ scores to be correlated. However, as the authors themselves say, retrospective reports are “highly fallible.” Also, one might question whether the responses to Likert scale questions about forgetting names and so on actually measure what is fair to call “paying attention.”
So what do we conclude? Well, if the evidence holds up, it suggests two possible approaches to accident reduction: developing systems that are robust despite poor attention from users, and developing systems for keeping operators’ minds from wandering. My vote is on the former as the most effective. As Di Nocera et al. point out, the latter is made more difficult by increasing automation of vehicles and other systems that people use. We face the irony that making things easier to use risks causing the operator’s mind to wander, which, in turn, causes accidents.
Finally, we have an article from the University of Calgary in Alberta, Canada. Justin Baers and colleagues show how human factors can be effectively applied to medical procedures. They performed simulations of Ebola patients arriving at hospitals. The simulations were “real-world,” in that the simulated patients were “treated” among real patients in real facilities. The authors videotaped the simulations and then analyzed them for “behaviors that could lead to contamination,” collaborating closely with infection-control personnel. These simulations, really a variant of in situ usability testing, resulted in 123 recommendations for improvements, demonstrating the value of adapting methods commonly applied to the usability and safety of medical devices to improving larger-scale medical procedures. Needless to say, there is great value in identifying potential problems and improving procedures prior to, rather than after, an Ebola outbreak.
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