{
  "abstract": "Objective Coding free-text job descriptions is time-consuming and expensive. We evaluated the use of the SOCcer auto-coding tool to help participants self-code their occupations in an online questionnaire of the population-based Connect for Cancer Prevention Study.Material and Methods We used SOCcer to provide participants with a list of the four best-fitting occupation categories based on self-reported job title. Participants were asked to select the best category or ‘none of the above’ (v1). In a second version of the questionnaire, we incorporated answers to a work task question and expanded the list to six suggestions (v2). An expert coded 1,000 randomly selected ‘none of the above’ jobs from each version to the 6-digit level where possible, blind to SOCcer’s suggestions. We examined the frequency of selecting a SOCcer-suggested code. We also evaluated the expert’s ability to code the ‘none of the above’ jobs and compared the expert’s assigned code to SOCcer’s suggested list.Results V2 modifications reduced the selection of ‘none of the above’ from 17% of 23,699 v1 jobs to 14% of 12,060 v2 jobs. The ‘none of the above’ jobs had lower median SOCcer scores (v1=0.15; v2=0.16) than the participant-coded jobs (v1=0.53; v2=0.56). For these ‘none of the above’ jobs, incorporating task increased the proportion of jobs the expert could code to 6-digits from 68% in v1 to 95% in v2 and decreased un-codable jobs from 16% to 3%; the remainder were assigned a less detailed code. The expert-assigned code was within the SOCcer-suggested list 27% and 54% of the time in v1 and v2, respectively.Conclusion Participants successfully self-coded most jobs, with improved performance observed when we added work tasks and expanded the list of suggested occupations. Incorporating task also improved the expert’s ability to assign the ‘none of the above’ jobs to a detailed occupation code.",
  "authors": [
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute – National Institutes of Health"
      ],
      "name": "Pabitra R Josse"
    },
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute – National Institutes of Health"
      ],
      "name": "Daniel E Russ"
    },
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute – National Institutes of Health"
      ],
      "name": "Leila Orszag"
    },
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute – National Institutes of Health"
      ],
      "name": "Nicole Gerlanc"
    },
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute – National Institutes of Health"
      ],
      "name": "Jonas Almeida"
    },
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute – National Institutes of Health"
      ],
      "name": "Laura E Beane Freeman"
    },
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute – National Institutes of Health"
      ],
      "name": "Melissa C Friesen"
    }
  ],
  "title": "8274114 Using the soccer occupation auto-coding tool to help participants self-code their occupation in online questionnaires",
  "uid": "26cf9f17-7a39-5a2b-880f-199b44bb4b94"
}