Impact of Variability in Job Coding on Reliability in Exposure Estimates Obtained via a Job-Exposure Matrix
Bibliographic record
Abstract
OBJECTIVES: The use of a job-exposure matrix (JEM) to assess exposure to potential health hazards in occupational epidemiological studies requires coding each participant's job history to a standard occupation and/or industry classification system recognized by the JEM. The objectives of this study were to assess the impact of inter-coder variability in job coding on reliability in exposure estimates derived from linking the job codes to the Canadian job-exposure matrix (CANJEM) and to identify influent parameters. METHOD: Two trained coders independently coded 1000 jobs sampled from a population-based case-control study to the ISCO-1968 occupation classification at the five-digit resolution level, of which 859 could be linked to CANJEM using both assigned codes. Each of the two sets of codes was separately linked to CANJEM and thereby generated, for each of the 258 occupational agents available in CANJEM, two exposure estimates: exposure status (yes/no) and intensity of exposure (low, medium, and high) for exposed jobs only. Then, inter-rater reliability (IRR) was computed (i) after stratifying agents in 4 classes depending, for each, on the proportion of occupation codes in CANJEM defined as 'exposed' and (ii) for two additional scenarios restricted to jobs coded differently: the first one using experts' codes, the other one using codes randomly selected. IRR was computed using Cohen's kappa, PABAK and Gwet's AC1 index for exposure status, and weighted kappa and Gwet's AC2 for exposure intensity. RESULTS: Across all agents and based on all jobs, median (Q1, Q3; Nagents) values were 0.68 (0.59, 0.75; 220) for kappa, 0.99 (0.95, 1.00; 258) for PABAK, and 0.99 (0.97, 1.00; 258) for AC1. For the additional scenarios, median kappa was 0.28 (0.00, 0.45; 209) and -0.01 (-0.02, 00; 233) restricted to jobs coded differently using experts' and random codes, respectively. A similar decreasing pattern was observed for PABAK and AC1 albeit with higher absolute values. Median kappa remained stable across exposure prevalence classes but was more variable for low prevalent agents. PABAK and AC1 decreased with increasing prevalence. Considering exposure intensity and all exposed jobs, median values were 0.79 (0.68, 0.91; 96) for weighted kappa, and 0.95 (0.89, 0.99; 102) for AC2. For the additional scenarios, median kappa was, respectively, 0.28 (-0.04, 0.42) and -0.05 (-0.18, 0.09) restricted to jobs coded differently using experts' and random codes, with a similar though attenuated pattern for AC2. CONCLUSION: Despite reassuring overall reliability results, our study clearly demonstrated the loss of information associated with jobs coded differently. Especially, in cases of low exposure prevalence, efforts should be made to reliably code potentially exposed jobs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.110 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".