2013
DOI: 10.3357/asem.3176.2013
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Exploring Human Error in Military Aviation Flight Safety Events Using Post-Incident Classification Systems

Abstract: Skill-based errors in military operations are more prevalent in rotary wing incidents and are related to higher level supervisory processes in the organization. The Cognitive Error Taxonomy provides increased granularity to HFACS analyses of unsafe acts.

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Cited by 28 publications
(19 citation statements)
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“…Such taxonomies should be tailored to the industry and should utilize human factors concepts to codify data on the types of incident experienced by operators (e.g., their technical nature, their outcomes), the workplace problems that lead to them (e.g., human–computer interfaces), and the skills and behaviors important for a work domain (e.g., in team vs. noncollaborative roles). The data collected can be used to collect headline data on incident occurrences within a given industry—for example, that in surgery, 43% of incidents involve team communication problems ( Gawande, Zinner, Studdert, & Brennan, 2003 ) or that in military aviation, errors are more likely in rotary than in fixed-wing aircraft ( Hooper & O’Hare, 2013 ). Furthermore, incident reporting is used to identify in-depth data on the causes of specific forms of mishap that can be used to develop interventions (e.g., new software, training), or for example, aspects of system design that lead to errors in the flight cockpit ( Billings, 1999 ; Moura, Beer, Patelli, Lewis, & Knoll, 2016 ) or aspects of clinician behavior that either contributed to an adverse event (e.g., loss of situation awareness) or helped to avert it (e.g., teamwork skills; Schulz, Endlsey, Kochs, Gelb, & Wagner, 2013 ; Undre, Sevdalis, Healey, Darzi, & Vincent, 2007 ).…”
Section: Introductionmentioning
confidence: 99%
“…Such taxonomies should be tailored to the industry and should utilize human factors concepts to codify data on the types of incident experienced by operators (e.g., their technical nature, their outcomes), the workplace problems that lead to them (e.g., human–computer interfaces), and the skills and behaviors important for a work domain (e.g., in team vs. noncollaborative roles). The data collected can be used to collect headline data on incident occurrences within a given industry—for example, that in surgery, 43% of incidents involve team communication problems ( Gawande, Zinner, Studdert, & Brennan, 2003 ) or that in military aviation, errors are more likely in rotary than in fixed-wing aircraft ( Hooper & O’Hare, 2013 ). Furthermore, incident reporting is used to identify in-depth data on the causes of specific forms of mishap that can be used to develop interventions (e.g., new software, training), or for example, aspects of system design that lead to errors in the flight cockpit ( Billings, 1999 ; Moura, Beer, Patelli, Lewis, & Knoll, 2016 ) or aspects of clinician behavior that either contributed to an adverse event (e.g., loss of situation awareness) or helped to avert it (e.g., teamwork skills; Schulz, Endlsey, Kochs, Gelb, & Wagner, 2013 ; Undre, Sevdalis, Healey, Darzi, & Vincent, 2007 ).…”
Section: Introductionmentioning
confidence: 99%
“…13 Omission errors were defined as failure to perform a step entirely. Commission errors represented failure to perform a step correctly.…”
Section: Methodsmentioning
confidence: 99%
“…The field of human factors engineering (HFE) has a long history in developing error-based assessments to improve human performance in high-risk and complex industries including aviation 13 and nuclear energy. 14 HFE is the scientific discipline concerned with understanding interactions among humans and other elements of a system in order to optimize human well-being and overall system performance.…”
Section: Introductionmentioning
confidence: 99%
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“…HFACS has also been extended to the proactive management and prediction of incidents through causal pathway analysis in the mining ( Lenné et al, 2012 ), process ( Baldissone et al, 2019 ), aviation ( Hooper and O’Hare, 2013 , Inglis et al, 2010 , Li et al, 2008 , Liu et al, 2013 ), oil and gas ( Theophilus et al, 2017 ), and construction ( Sun et al, 2011 , Ye et al, 2018 ) industries. Neural networks have also been used to predict the unsafe acts (Level 1 errors) from preconditions of unsafe acts (Level 2 errors) (Harris and Li, 2019) and even classify HFACS nanocodes from text data ( Madeira et al, 2021 ).…”
Section: Human Factors Analysis and Classification System (Hfacs)mentioning
confidence: 99%