Sociodemographic Determinants of Occupational Risks of Exposure to COVID‐19 in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The activities performed by Canadian workers in some occupations may increase the risk of exposure to infectious diseases such as COVID-19. This research note explores how occupational exposure risks vary by labor force characteristics using publicly available Canadian data in combination with a data set providing information on the level of physical proximity and frequency of exposure to infections or diseases faced by workers in different occupations. The results show important sociodemographic differences. First, women work in occupations associated with significantly higher average risks of exposure to COVID-19 than men. This is driven by their overrepresentation in high-risk broad occupational categories such as health occupations. Second, older workers (65 years or more), a group vulnerable to COVID-19, appear to work in occupations requiring performing activities characterized by a lower level of physical proximity than their younger colleagues, with minimal differences in the frequency of exposure to diseases or infections. Finally, workers in low-income occupations are employed in occupations that put them at greater risk of exposure to COVID-19 than other workers. This is especially the case for women, immigrants, and members of visible minority groups in low-income occupations. More broadly, this research note provides insights into the health-related dimension of the literature on occupational tasks and labor market stratification.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it