Bibliographic record
Abstract
This study assessed the relationship between race and long COVID and the role that socioeconomic plays in this relationship. We analyzed data from the Household Pulse Survey (HPS) conducted by the U.S. Census Bureau from September 14 to September 26, 2022. Of the 18,061 individuals in the sample, 4,927 (weighted 28.6 percent) reported long COVID. We used multiple logistic regressions to examine the association between race, socioeconomic status, and long COVID. We found that Black and Hispanic individuals shared similar odds of long COVID with White individuals. Only Asian individuals reported a significantly lower odds of long COVID as compared to White individuals. The relationship between race and long COVID was buffered by socioeconomic status ( p-value <.001), but the effect size was 3 times greater among White individuals than among Black, Hispanic, and Asian individuals. These findings suggest that support for groups with long COVID should especially be concentrated among individuals with low socioeconomic status. It is also important to address the barriers that limit the translation of high socioeconomic status into a protective health resource for racial and ethnic minorities.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".