Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies.
Objectives Computer-assisted coding of job descriptions to standardized occupational classification codes facilitates evaluating occupational risk factors in epidemiologic studies by reducing the number of jobs needing expert coding. We evaluated the performance of the 2nd version of SOCcer, a compu...
| Publicado en: | Annals of Work Exposures & Health Vol. 67; no. 6; pp. 772 - 784 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2023
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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=164969623&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164969623 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23987308 KJ1E jtl: Annals of Work Exposures & Health issn: 23987308 maglogo: N pubinfo: dt: Jul2023 vid: 67 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 164969623 164969623 164969623 10.1093/annweh/wxad020 164969623 ppf: 772 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies. aug: au: Russ, Daniel E Josse, Pabitra Remen, Thomas Hofmann, Jonathan N Purdue, Mark P Siemiatycki, Jack Silverman, Debra T Zhang, Yawei Lavoué, Jerome Friesen, Melissa C affil: Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute , Bethesda, MD , United States sug: subj: Coding, Computer-Assisted Utilization Algorithms Utilization Occupational Exposure Adverse Effects Occupational Diseases Risk Factors Risk Assessment Occupational Diseases Epidemiology Human Funding Source Comparative Studies Descriptive Statistics United Kingdom ab: Objectives Computer-assisted coding of job descriptions to standardized occupational classification codes facilitates evaluating occupational risk factors in epidemiologic studies by reducing the number of jobs needing expert coding. We evaluated the performance of the 2nd version of SOCcer, a computerized algorithm designed to code free-text job descriptions to US SOC-2010 system based on free-text job titles and work tasks, to evaluate its accuracy. Methods SOCcer v2 was updated by expanding the training data to include jobs from several epidemiologic studies and revising the algorithm to account for nonlinearity and incorporate interactions. We evaluated the agreement between codes assigned by experts and the highest scoring code (a measure of confidence in the algorithm-predicted assignment) from SOCcer v1 and v2 in 14,714 jobs from three epidemiology studies. We also linked exposure estimates for 258 agents in the job-exposure matrix CANJEM to the expert and SOCcer v2-assigned codes and compared those estimates using kappa and intraclass correlation coefficients. Analyses were stratified by SOCcer score, score distance between the top two scoring codes from SOCcer, and features from CANJEM. Results SOCcer's v2 agreement at the 6-digit level was 50%, compared to 44% in v1, and was similar for the three studies (38%–45%). Overall agreement for v2 at the 2-, 3-, and 5-digit was 73%, 63%, and 56%, respectively. For v2, median ICCs for the probability and intensity metrics were 0.67 (IQR 0.59–0.74) and 0.56 (IQR 0.50–0.60), respectively. The agreement between the expert and SOCcer assigned codes linearly increased with SOCcer score. The agreement also improved when the top two scoring codes had larger differences in score. Conclusions Overall agreement with SOCcer v2 applied to job descriptions from North American epidemiologic studies was similar to the agreement usually observed between two experts. SOCcer's score predicted agreement with experts and can be used to prioritize jobs for expert review. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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