Jointly applying a work simulator and ATOM to prevent occupational accidents and MSD through workforce selection

Background and Aims: The goal of our work is the presentation of a particular – scientifically well-established – concept aiming to predict the propensity of individual job candidates for causing or suffering workplace accidents, and also for MSD-type (Musculoskeletal Disorder) occupational diseases, by further processing the performance parameters obtained by a work simulator (like ErgoScope) with the help of ATOM. Methods: After introducing the problems of workplace accidents and MSDs, and critically reviewing the basic literature related to the so-called “work sample tests” and work simulators, the application possibilities of a specific, general-purpose work simulator, the ErgoScope, are presented for our purposes. After that, the possibilities of adequately integrating the ErgoScope and ATOM are described with particular respect to workplace accidents and MSDs, illustrated through a fictitious but realistically specified example. Conclusions: The purposeful combination of the ErgoScope work simulator with ATOM can have a “synergistic” effect that reinforces each other’s effects, contributing to a significant reduction in the likelihood of workplace accidents and MSDs. Simply put, we propose to apply the appropriate outputs of the ErgoScope work simulator as inputs to ATOM

Illustrating real-life ATOM application case studies

Background and Aims: Presenting real-life ATOM application field studies to illustrate how ATOM should be applied in the practice of workforce selection. Methods: After having defined applied metrics for assessing the categorization performance of ATOM, and – for simplicity, reliability and uniformity reasons – confining ourselves to binary job success scales. Five concrete real-life ATOM application field studies are presented basically in tabular form. Discussion: It can be stated that (1) ATOM is susceptible to data quality, therefore pertinent job success and predictor data are needed; (2) the sample sizes must always be at least about 100; (3) the free choice of cut-off points on the label probability scales, as necessary, is an effective method for finding the best solution