Estimating the number of Korean workers at high risk of COVID-19 infection
Bibliographic record
Abstract
OBJECTIVES: We aimed to identify occupational groups at high-risk of coronavirus disease 2019 (COVID-19) infection in Korea, to estimate the number of such workers, and to examine the prevalence of protective resources by employment status. METHODS: Based on the sixth Standard Occupational Classification codes, 2015 census data were linked with data from the fifth Korean Working Conditions Survey, which measured how frequently workers directly come into contact with people other than fellow employees in the workplace. RESULTS: A total of 30 occupational groups, including 7 occupations from the healthcare and welfare sectors and 23 from other sectors, were classified as high-risk occupational groups involving frequent contact with people other than fellow employees in the workplace (more than half of the working hours). Approximately 1.4 million (women, 79.1%) and 10.7 million workers (46.3%) are employed in high-risk occupations. Occupations with a larger proportion of women are more likely to be at a high-risk of infection and are paid less. For wage-earners in high-risk occupations, protective resources to deal with COVID-19 (e.g., trade unions and health and safety committees) are less prevalent among temporary or daily workers than among those with permanent employment. CONCLUSIONS: Given the large number of Koreans employed in high-risk occupations and inequalities within the working population, the workplace needs to be the key locus for governmental actions to control COVID-19, and special consideration for vulnerable workers is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".