Magnifying the importance of collecting race, ethnicity, industry, and occupation data during the COVID-19 pandemic
Bibliographic record
Abstract
The contagiousness of coronavirus disease-2019 (COVID-19) led to the imposition of historical lockdowns in various countries. No scientific mind could have made accurate projections of the tremendous impact that COVID-19 would have on nations, communities, and the global-wide economy. Meanwhile, millions of workers have lost their jobs, while healthcare workers are overwhelmed and are reaching a state of mental and physical exhaustion. With the uncontrollable spread, researchers have been working to identify factors associated with COVID-19. In this regard, race, ethnicity, industry, and occupation have been found to be predominant factors of interest. However, unfortunately, the unavailability of such information has been a difficult reality. Since race, ethnicity, and employment are essential social determinants of health and could serve as potential risk-factors for COVID-19, collecting such information may offer important context for prioritising vulnerable groups. Thus, this perspective aims to highlight the importance and need for collecting race, ethnicity, and occupation-related data to track and treat the racial/ethnic groups that have been most strongly affected by the COVID-19 pandemic. Collecting such data will provide valuable insights and help public health officials recognise workplace-related outbreaks and evaluate the odds of various ethnic groups and professions contracting COVID-19.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".