Near-miss narratives from the fire service: A Bayesian analysis

Accident Analysis and Prevention

Volumn 62, Issue , 2014, Pages 119-129

  Taylor, Jennifer A ^a   Lacovara, Alicia V ^a   Smith, Gordon S ^b   Pandian, Ravi ^a   Lehto, Mark ^c  

^a DREXEL UNIVERSITY   (United States)

^b UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

^c SCHOOL OF INDUSTRIAL ENGINEERING   (United States)

Author keywords

Bayesian models; Fire fighter injury; Near miss narratives; Text mining

Indexed keywords

BAYESIAN MODEL; CALCULATED SENSITIVITY; FIRE FIGHTERS; MECHANISM OF INJURY; NEAR-MISS NARRATIVES; POSITIVE PREDICTIVE VALUES; PREDICTIVE ABILITIES; TEXT-MINING; BAYESIAN ANALYSIS; FIRE SERVICES;

BAYESIAN NETWORKS; EMERGENCY SERVICES; FORECASTING;

ACCIDENT PREVENTION;

ARTICLE; BAYES THEOREM; BAYESIAN MODELS; DATA MINING; EMERGENCY HEALTH SERVICE; FIRE FIGHTER; FIRE FIGHTER INJURY; HUMAN; METHODOLOGY; NEAR-MISS NARRATIVES; OCCUPATIONAL ACCIDENT; OCCUPATIONAL HEALTH; STATISTICAL MODEL; STATISTICS; TEXT-MINING; UNITED STATES; VERBAL COMMUNICATION;

BAYESIAN MODELS; FIRE FIGHTER INJURY; NEAR-MISS NARRATIVES; TEXT-MINING;

ACCIDENTS, OCCUPATIONAL; BAYES THEOREM; DATA MINING; EMERGENCY MEDICAL SERVICES; FIREFIGHTERS; HUMANS; MODELS, STATISTICAL; NARRATION; OCCUPATIONAL HEALTH; OCCUPATIONAL INJURIES; UNITED STATES;

EID: 84886709546     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2013.09.012     Document Type: Article

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