Underreporting of race/ethnicity in COVID-19 research
Bibliographic record
Abstract
OBJECTIVES: Although racial/ethnic disparities in healthcare have long been recognized, recent discourse around structural racism will hopefully lead to improved transparency surrounding these issues. Despite the disproportionate impact of COVID-19 on racial/ethnic minorities, the extent and reliability of race reporting in COVID research is unclear. METHODS: COVID-19 research published in three top medical journals during the first wave of the COVID-19 pandemic was reviewed and assessed for race reporting and proportional representation. RESULTS: Of the 95 manuscripts that were identified, 56 reporting on 252,262 patients met eligibility. Thirty-five (62.5%) did not report race distribution and 15 (26.7%) did not report ethnicity. There was no difference based on journal (P = 0.87), study sponsor (P = 0.41), whether the study was retrospective or prospective (P = 0.33), or observational vs interventional (P = 0.11). Studies with ≥250 patients were more likely to report on race (OR 4.01, 95% CI: 1.12-14.37, P = 0.027), and North American (USA and Canada) studies were more likely than European studies (OR 7.88, 95% CI: 1.73-37.68, P = 0.006) to report on race. COVID-19 research mirrored USA COVID-19 racial incidence; however, both showed higher distribution of COVID-19 infection among Blacks and a smaller proportion of Whites compared to the USA population. This suggests that research is broadly representing infection rates and that social determinants of health are impacting racial distribution of infection. CONCLUSIONS: Despite increasing awareness of racial disparities and inequity, COVID-19 research during the first wave of the pandemic lacked appropriate racial/ethnicity reporting. However, research mirrored COVID-19 incidence in the USA, with an increased burden of infection among Black individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.423 | 0.633 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".