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A simple model of human performance with four stages of error detection 

A simple model of human performance with four stages of error detection 

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In recent years, there has been a realization that total elimination of human error may be difficult to achieve. A further reduction of accidents will require a better understanding of how practitioners manage their errors in ways that consequences are contained or mitigated. With this goal in mind, the present study has set out to propose a framew...

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Context 1
... put into perspective the various cognitive strategies involved in detecting errors at the conceptual and execution stages, Figure 1 presents a simple model of human performance that encompasses four stages: (1) assessment of the situation, (2) formulation of a plan of action, (3) rehearsal, execution and adaptation of the plan and (4) evaluation of the outcome of performance based upon system feedback. The four stages are performed in a circular fashion, so that feedback of performance can be used to alter an existing goal or modify a previous assessment of the situation. ...
Context 2
... Figure 1 here This is a nonlinear model of performance since there is no need for practitioners to complete situation assessment before decision making. Practitioners may live up with some uncertainty about the situation and make a decision how to tackle the problem at an early stage; as more evidence becomes available, the situation can be revised. ...
Context 3
... main argument, put forward in this paper, is that human error should not be seen as a failure of cognitive resources to make a correct assessment or develop a correct plan in the first place. Figure 1 presents an incremental view of performance where an assessment can tolerate certain sources of uncertainty and proceed with a plan of action while remaining vigilant to new evidence. Detecting errors in situation assessment amounts to handling data uncertainty so that the assessment becomes more accurate and reliable. ...
Context 4
... sources of uncertainty faced by operators at different stages of performance (as shown in Figure 1) include: incomplete and conflicting data, goal tradeoffs, need to modify plans quickly, acting and monitoring simultaneously, masking effects and automation effects. Table 1 Antonogiannakis, 2003;Mitsotakis, 2006). ...

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Chapter
Human error has contributed considerably to accidents in various working environments. In the field of human error and reliability, environmental factors are termed performance-shaping factors, error-forcing conditions, common performance conditions. This chapter focuses on the cognitive mechanisms related to the occurrence of human errors. This theory foundation can serve as the technical basis for further research, such as error detection, error prediction, error analysis, error correction, etc. Human error classification is fundamental to human error research, investigation, prediction, detection, analysis, and control. For human reliability analysis (HRA) practitioners, its most important function is to quantify human error probabilities in a task or scenario of interest in risk assessments. In other domains in which human errors could be a great source of vulnerability, HRA also has many considerations and adoptions, such as oil and gas, aviation, spaceflight, health care and surgery, railways, cybersecurity, and human-autonomy interaction.
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Thesis
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Background: Low morale is classified as a latent condition for performance variability in safety-critical environments. Morale management may assist in the control of performance variability as part of a systems approach to safety. A context-specific model for measuring and managing morale with reference to followership in a safety-critical air traffic control (ATC) environment could not be found. Purpose/Aim: The purpose of this study was to develop a model that enables the measurement and management of air traffic controller (ATCO) team morale. Research Design: An exploratory sequential mixed method design was adopted. A census approach to sampling was used to conduct 21 focus group sessions as the qualitative phase, providing the definition and drivers of morale. The Measure of Morale and its Drivers (MoMaD) survey instrument was created from qualitative data, then administered to 256 ATCOs in the quantitative phase. Statistical methods included exploratory factor analysis, correlation and regression analysis to construct the final MoMaD model. Results: A context-specific definition of morale is provided and communication management, team cohesion, leadership interaction, staff incentive, staffing level, workplace health and safety and mutual trust were found to be the drivers of morale in a safety-critical ATC environment. A single-item measure of perceived morale reflected the state of context-specific ATCO team morale more accurately than an existing generalisable multi-item measure. Conclusion: This study contributes to the body of knowledge by integrating applicable aspects of morale, followership, performance variability and organisational culture and climate in safety-critical ATC environments into a new theoretical framework. The MoMaD instrument is presented as a context-specific model for measuring and managing ATCO team morale in an ATC environment. Recommendations: Future research opportunities include the possible influence of morale as a predictor of morale in safety-critical environments and the development of a context-specific multi-item measure of morale for integration into the MoMaD model. Key terms: morale; followership; human error; human performance variability; air traffic control; aviation; corporate culture; safety culture; esprit de corps; motivation
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Errors in examination papers and other assessment instruments can compromise fairness. For example, a history question containing an incorrect historical date could be impossible for students to answer. Incorrect instructions at the start of an examination could lead students to answer the wrong number of questions. As there is little research on this issue within the educational assessment community, we reviewed the literature on minimizing errors in other industries and domains, including aviation, energy, and medicine. We identified generalizable principles and applied them to our context of educational assessment. We argue that since assessment instrument construction is a complex system comprising numerous interacting components, a holistic approach to system improvement is required. Assessment instrument errors stem primarily from human failure. When human failure occurs, it is not good enough to suggest that ‘to err is simply human’. Instead it is necessary to look deeper, evaluating the latent working conditions that underpin the efficacy of procedures, making the human failure more or less likely. Drawing from the aviation industry’s ergonomic SHELLO model, we articulate and explore three of the most critical working conditions that relate to our context: (i) time pressure, (ii) workload and stress, and (iii) wider organizational culture, including good error data collection. We conclude with recommendations for best practice in minimizing errors in assessment instruments. A ‘good’ error culture should be promoted, which avoids blaming individuals. Errors should be acknowledged readily by all, and system owners should take a scientific approach to understanding and learning from them.