Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison.
Objectives Automatic 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...
| Publicado en: | Annals of Work Exposures & Health Vol. 67; no. 5; pp. 663 - 673 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164368323&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164368323 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23987308 KJ1E jtl: Annals of Work Exposures & Health issn: 23987308 maglogo: N pubinfo: dt: Jun2023 vid: 67 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 164368323 164368323 164368323 10.1093/annweh/wxad002 164368323 ppf: 663 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison. aug: au: Wan, Wenxin Ge, Calvin B Friesen, Melissa C Locke, Sarah J Russ, Daniel E Burstyn, Igor Baker, Christopher J O Adisesh, Anil Lan, Qing Rothman, Nathaniel Huss, Anke Tongeren, Martie van Vermeulen, Roel Peters, Susan affil: Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University , Utrecht , The Netherlands sug: subj: Job Description Coding Methods Automation Work Assignments Human Comparative Studies Occupational Health Occupational Exposure Job Performance Descriptive Statistics Funding Source Reliability ab: Objectives Automatic 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. Methods We 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. Results The 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). Conclusions In 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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