ArticleAnnals of work exposures and health2023
Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison.
Article in Annals of work exposures and health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Evaluation of using the SOCcer automated occupation auto-coding algorithm to assist expert coding of job descriptions.Annals of work exposures and health · 2026Article
- Data for assigning a proxy variable for office worker in open-ended responses on occupation in Swedish questionnaires.Data in brief · 2025Article
- OPERAS decision support system versus manual job coding: a quantitative analysis on coding time and inter-coder reliability.Occupational and environmental medicine · 2025Article
- Development of standard job classification codes for building a job-exposure matrix for police officers.Annals of occupational and environmental medicine · 2025Article
- Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys.Annals of occupational and environmental medicine · 2024Article
- Artificial intelligence exceeds humans in epidemiological job coding.Communications medicine · 2023Article
- Invited Perspective: How Far Have We Come? Revisiting a 2009 Report on Occupational Cancer Research Recommendations.Environmental health perspectives · 2023Article
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Authors and funding
14 authors.
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Abstract
objectivesAutomatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools.
methodsWe compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools.
resultsThe coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8-58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69-0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61).
conclusionsIn this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools.
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