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Near-miss narratives from the fire service: A Bayesian analysis
Journal article   Open access   Peer reviewed

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

Jennifer A Taylor, Alicia V Lacovara, Gordon S Smith, Ravi Pandian and Mark Lehto
Accident analysis and prevention, v 62, pp 119-129
Jan 2014
DOI: https://doi.org/10.1016/j.aap.2013.09.012
PMID: 24144497
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.aap.2013.09.012View
Published, Version of Record (VoR) Open

Abstract

Bayesian models Text-mining Near-miss narratives Fire fighter injury
•Bayesian models successfully assigned injury cause codes to near-miss narratives.•The Fuzzy and Naïve models performed well on lengthy narratives.•The Fuzzy model performed at a higher sensitivity than the Naïve model.•The models performed well with little effort in optimizing their performance.•Autocoding methods enabled the creation of two new quantitative data elements. In occupational safety research, narrative text analysis has been combined with coded surveillance, data to improve identification and understanding of injuries and their circumstances. Injury data give, information about incidence and the direct cause of an injury, while near-miss data enable the, identification of various hazards within an organization or industry. Further, near-miss data provide an, opportunity for surveillance and risk reduction. The National Firefighter Near-Miss Reporting System, (NFFNMRS) is a voluntary reporting system that collects narrative text data on near-miss and injurious, events within the fire and emergency services industry. In recent research, autocoding techniques, using Bayesian models have been used to categorize/code injury narratives with up to 90% accuracy, thereby reducing the amount of human effort required to manually code large datasets. Autocoding, techniques have not yet been applied to near-miss narrative data. We manually assigned mechanism of injury codes to previously un-coded narratives from the, NFFNMRS and used this as a training set to develop two Bayesian autocoding models, Fuzzy and Naïve. We calculated sensitivity, specificity and positive predictive value for both models. We also evaluated, the effect of training set size on prediction sensitivity and compared the models’ predictive ability as, related to injury outcome. We cross-validated a subset of the prediction set for accuracy of the model, predictions. Overall, the Fuzzy model performed better than Naïve, with a sensitivity of 0.74 compared to 0.678., Where Fuzzy and Naïve shared the same prediction, the cross-validation showed a sensitivity of 0.602., As the number of records in the training set increased, the models performed at a higher sensitivity, suggesting that both the Fuzzy and Naïve models were essentially “learning”. Injury records were, predicted with greater sensitivity than near-miss records. We conclude that the application of Bayesian autocoding methods can successfully code both near misses, and injuries in longer-than-average narratives with non-specific prompts regarding injury. Such, coding allowed for the creation of two new quantitative data elements for injury outcome and injury, mechanism.

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44 citations in Web of Science
52 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

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Collaboration types
Domestic collaboration
Web of Science research areas
Ergonomics
Public, Environmental & Occupational Health
Social Sciences, Interdisciplinary
Transportation
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