ArticleOccupational and environmental medicine2016
Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies.
Article in Occupational and environmental medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 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
- Applying a NIOSH Industry and Occupation Coding tool to evaluate occupational risk of SARS-CoV-2 infection in pregnant women.Discover public health · 2026Article
- Making strides in doctoral-level career outcomes reporting: a review of classification and visualization methodologies in graduate education.Frontiers in education · 2025Article
- Occupational exposure to organic solvents and risk of bladder cancer.Journal of exposure science & environmental epidemiology · 2024Article
- Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys.Annals of occupational and environmental medicine · 2024Article
- Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.PloS one · 2024Article
- Standard Occupational Classification Codes: Gaps in Federal Data on the Public Health Workforce.American journal of public health · 2024Review
- Artificial intelligence exceeds humans in epidemiological job coding.Communications medicine · 2023Article
- Quest markup for developing FAIR questionnaire modules for epidemiologic studies.BMC medical informatics and decision making · 2023Article
- Occupational models from 42 million unstructured job postings.Patterns (New York, N.Y.) · 2023Article
- Prerequisite for Imputing Non-detects among Airborne Samples in OSHA's IMIS Databank: Prediction of Sample's Volume.Annals of work exposures and health · 2023Article
- Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies.Annals of work exposures and health · 2023Article
- Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison.Annals of work exposures and health · 2023Article
- Occupational history associates with ALS survival and onset segment.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2023Article
- Associations of self-reported occupational exposures and settings to ALS: a case-control study.International archives of occupational and environmental health · 2022Article
- A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome.International journal of environmental research and public health · 2022Review
- Determining occupation for National Violent Death Reporting System records: An evaluation of autocoding programs.American journal of industrial medicine · 2021Article
- Labour market competition for public health graduates in the United States: A comparison of workforce taxonomies with job postings before and during the COVID-19 pandemic.The International journal of health planning and management · 2021Article
- Occupation Coding of Job Titles: Iterative Development of an Automated Coding Algorithm for the Canadian National Occupation Classification (ACA-NOC).JMIR formative research · 2020Article
- The Future of Careers at the Intersection of Climate Change and Public Health: What Can Job Postings and an Employer Survey Tell Us?International journal of environmental research and public health · 2020Article
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Authors and funding
15 authors.
Funding
Abstract
backgroundMapping job titles to standardised occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiological studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components.
methodsJob title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes. An industry-based classifier was developed based on the SOC prevalence within an industry. These classifiers were used in a logistic model trained using 14 983 jobs with expert-assigned SOC codes to obtain empirical weights for an algorithm that scored each SOC/job description. We assigned the highest scoring SOC code to each job. SOCcer was validated in 2 occupational data sources by comparing SOC codes obtained from SOCcer to expert assigned SOC codes and lead exposure estimates obtained by linking SOC codes to a job-exposure matrix.
resultsFor 11 991 case-control study jobs, SOCcer-assigned codes agreed with 44.5% and 76.3% of manually assigned codes at the 6-digit and 2-digit level, respectively. Agreement increased with the score, providing a mechanism to identify assignments needing review. Good agreement was observed between lead estimates based on SOCcer and manual SOC assignments (κ 0.6-0.8). Poorer performance was observed for inspection job descriptions, which included abbreviations and worksite-specific terminology.
conclusionsAlthough some manual coding will remain necessary, using SOCcer may improve the efficiency of incorporating occupation into large-scale epidemiological studies.
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