Kathleen Van Benthem, Kirsten Brightman, Elizabeth Riguero, Chris M. Herdman
{"title":"使用 \"CANFLY:飞行员认知健康筛查工具 \"对高风险飞行员进行分类的结果和方法","authors":"Kathleen Van Benthem, Kirsten Brightman, Elizabeth Riguero, Chris M. Herdman","doi":"10.1016/j.ergon.2024.103578","DOIUrl":null,"url":null,"abstract":"<div><p>Cognitive health screening for aviators would assist in managing a shortage of experienced pilots. Extending pilot careers by optimizing their cognitive health would address both the number and quality of pilots available for airline and general aviation operations. The present work tested the validity of an online screening tool for pilots that measures aviation domain-relevant cognition. Sixty-five licensed pilots (18–80 years, M = 48.8, SD = 16.3) with varying levels of experience completed a 30-min online cognitive health screening tool for pilots. Risk status was determined via a novel metric using self-reported incidents. Machine learning algorithms identified the cognitive factors most useful in identifying pilots with increased risk for accidents and serious incidents. Support vector machines and boosted decision tree algorithms provided the most reliable and strongest classifications models of pilot risk. Findings support the use of this short online screening tool for highlighting performance issues with domain-relevant cognitive abilities based on the Dynamic Mental Model for pilots, such as situation awareness and prospective memory. Understanding personal cognitive challenges is the basis for customized skill maintenance designed to augment cognition for those interested in safely extending their piloting careers.</p></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"101 ","pages":"Article 103578"},"PeriodicalIF":2.5000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169814124000349/pdfft?md5=c99b22194efca2c4332515bf4dbafccb&pid=1-s2.0-S0169814124000349-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Results and methodology for classifying high risk pilots using CANFLY: A cognitive health screening tool for aviators\",\"authors\":\"Kathleen Van Benthem, Kirsten Brightman, Elizabeth Riguero, Chris M. Herdman\",\"doi\":\"10.1016/j.ergon.2024.103578\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Cognitive health screening for aviators would assist in managing a shortage of experienced pilots. Extending pilot careers by optimizing their cognitive health would address both the number and quality of pilots available for airline and general aviation operations. The present work tested the validity of an online screening tool for pilots that measures aviation domain-relevant cognition. Sixty-five licensed pilots (18–80 years, M = 48.8, SD = 16.3) with varying levels of experience completed a 30-min online cognitive health screening tool for pilots. Risk status was determined via a novel metric using self-reported incidents. Machine learning algorithms identified the cognitive factors most useful in identifying pilots with increased risk for accidents and serious incidents. Support vector machines and boosted decision tree algorithms provided the most reliable and strongest classifications models of pilot risk. Findings support the use of this short online screening tool for highlighting performance issues with domain-relevant cognitive abilities based on the Dynamic Mental Model for pilots, such as situation awareness and prospective memory. Understanding personal cognitive challenges is the basis for customized skill maintenance designed to augment cognition for those interested in safely extending their piloting careers.</p></div>\",\"PeriodicalId\":50317,\"journal\":{\"name\":\"International Journal of Industrial Ergonomics\",\"volume\":\"101 \",\"pages\":\"Article 103578\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0169814124000349/pdfft?md5=c99b22194efca2c4332515bf4dbafccb&pid=1-s2.0-S0169814124000349-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Industrial Ergonomics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169814124000349\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Industrial Ergonomics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169814124000349","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Results and methodology for classifying high risk pilots using CANFLY: A cognitive health screening tool for aviators
Cognitive health screening for aviators would assist in managing a shortage of experienced pilots. Extending pilot careers by optimizing their cognitive health would address both the number and quality of pilots available for airline and general aviation operations. The present work tested the validity of an online screening tool for pilots that measures aviation domain-relevant cognition. Sixty-five licensed pilots (18–80 years, M = 48.8, SD = 16.3) with varying levels of experience completed a 30-min online cognitive health screening tool for pilots. Risk status was determined via a novel metric using self-reported incidents. Machine learning algorithms identified the cognitive factors most useful in identifying pilots with increased risk for accidents and serious incidents. Support vector machines and boosted decision tree algorithms provided the most reliable and strongest classifications models of pilot risk. Findings support the use of this short online screening tool for highlighting performance issues with domain-relevant cognitive abilities based on the Dynamic Mental Model for pilots, such as situation awareness and prospective memory. Understanding personal cognitive challenges is the basis for customized skill maintenance designed to augment cognition for those interested in safely extending their piloting careers.
期刊介绍:
The journal publishes original contributions that add to our understanding of the role of humans in today systems and the interactions thereof with various system components. The journal typically covers the following areas: industrial and occupational ergonomics, design of systems, tools and equipment, human performance measurement and modeling, human productivity, humans in technologically complex systems, and safety. The focus of the articles includes basic theoretical advances, applications, case studies, new methodologies and procedures; and empirical studies.