This paper is in the following e-collection/theme issue:

Ontologies, Classifications, and Coding (239)

Published on in Vol 4, No 1 (2016): Jan-Mar

Industry and Occupation in the Electronic Health Record: An Investigation of the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System

Industry and Occupation in the Electronic Health Record: An Investigation of the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System

Industry and Occupation in the Electronic Health Record: An Investigation of the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System

Authors of this article:

Matthew Schmitz1 ;   Linda Forst1 Author Orcid Image
Article Authors Cited by (18) Tweetations (4) Metrics

Journals

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  9. Scott E, Hirabayashi L, Graham J, Krupa N, Jenkins P. Using hospitalization data for injury surveillance in agriculture, forestry and fishing: a crosswalk between ICD10CM external cause of injury coding and The Occupational Injury and Illness Classification System. Injury Epidemiology 2021;8(1) View
  10. Varga A, Gruß I, Ritzwoller D, Bradley C, Sterrett A, Banegas M. Characterizing employment of colorectal cancer survivors using electronic health records. JAMIA Open 2021;4(3) View
  11. Kelmenson S. Between the farm and the fork: job quality in sustainable food systems. Agriculture and Human Values 2023;40(1):317 View
  12. Montoya‐Barthelemy A, Leniek K, Bannister E, Rushing M, Abrar F, Baumann T, Manly M, Wilhelm J, Niece A, Riester S, Kim H, Sellman J, Desai J, Anderson P, Bovard R, Pronk N, McKinney Z. Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting. American Journal of Industrial Medicine 2022;65(5):357 View
  13. Roberts B, Shkembi A, Smith L, Neitzel R. Beware the Grizzlyman: A comparison of job- and industry-based noise exposure estimates using manual coding and the NIOSH NIOCCS machine learning algorithm. Journal of Occupational and Environmental Hygiene 2022;19(7):437 View
  14. Bunn T, Liford M, Turner M, Bush A. Driver injuries in heavy vs. light and medium truck local crashes, 2010–2019. Journal of Safety Research 2022;83:26 View
  15. Russ D, Josse P, Remen T, Hofmann J, Purdue M, Siemiatycki J, Silverman D, Zhang Y, Lavoué J, Friesen M. Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies. Annals of Work Exposures and Health 2023;67(6):772 View
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  17. Howison M, Ensor W, Maharjan S, Parikh R, Sengamedu S, Daniels P, Gaither A, Yeats C, Reddy C, Hastings J. Extracting Structured Labor Market Information from Job Postings with Generative AI. Digital Government: Research and Practice 2024 View
  18. Oluwafemi O, Agopian A, Benjamin R, Gimeno Ruiz de Porras D, Shumate C, Patel J. Demographic Differences and Potential Bias From Automated Occupation Coding Among Mothers of Babies Born With or Without Cleft Lip and/or Cleft Palate in the Texas Birth Defects Registry. Journal of Occupational & Environmental Medicine 2024;66(10):839 View