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Demand Calibration in Multitask Environments: Interactions of Micro and Macrocognition
Published in Emily S. Patterson, Janet E. Miller, Macrocognition Metrics and Scenarios, 2018
Considering distraction-related mishaps as breakdowns of a multilevel control process highlights the role of demand calibration. Poor demand calibration undermines feedback, feedforward, and adaptive control, which contributes to distraction-related mishaps. This perspective also shows how micro and macrocognition can interact through cascade effects to undermine performance in multitask environments. Cascade effects occur when the outcome at one level of control affects control at another level. Such cascade effects reflect an interaction between macro and microcognition that is often neglected when the information-processing framework is used to describe distraction. Conventional microcognitive approaches (for example, resource theory) tend to ignore the contextual nesting of the multiple constraints at each of these levels. To understand driving performance it will be critical to understand how aspects of higher levels (for example, expectations about traffic) interact with lower levels (for example, sampling of information, or headway maintenance).
Examining medical-surgical nurse shift-to-shift handoffs to identify process, failures, and effects
Published in IISE Transactions on Healthcare Systems Engineering, 2019
Katherine Ernst, Sara McComb, Cathaleen Ley
Cascade effects are similar to what one might observe in a series of waterfalls, where disruption to the water flow of the first source consequentially impacts each subsequent waterfall. In this context, any disruption to an individual step may result in conditions causing deviations to subsequent steps of the process. Such an effect occurs when InNurses, failing to read the electronic health record prior to starting their shift, do not have a mental model or “patient profile” for use during the handoff conversation. Mental models are simplified representations of the world (Johnson-Laird, 1983) that humans construct to help them "describe, explain, and predict events in their environment[s]” (Mathieu et al., 2000, p. 274). The mental model or, as the nurses phrased it, “patient profile,” is used by the InNurse to counteract the effects of many failure modes later in the process. In particular, the mental model is used by the InNurse to compare and evaluate the content delivered during the OutNurse’s report; unmitigated failures during the report may decrease the accuracy of the InNurse’s mental model of the patient’s health and taskwork. Such failures can negatively affect the nurses’ decision making and care planning as they transition to nursing care activities. In other words, the early-process failure mode, “Neglecting to read the EHR,” enables a cascade of effects that could otherwise be alleviated.