Occupational risk prevention, education and support in black, Asian and ethnic minority health worker in the COVID-19 pandemic
Bibliographic record
Abstract
The onset of the COVID-19 in the UK has resulted in an inordinate amount of deaths affecting Black, Asian and Ethnic Minority (BAME) healthcare workers. The occupational risk to this group is thought to be a contributory factor, but other factors include race, genetics, medical co-morbidities, socio-economic status, and access to personal protection equipment. Why COVID-19 appears to be more deadly in BAME members remains unknown, but the UK government is investigating this now. It does appear that certain factors may worsen the disease process in BAME members, but which ones are pertinent to prevention remain to be determined, until a vaccine is available. Thus, the onus should rest on risk prevention, education, and support for all. Some of the safety strategies that may be instituted to help guide those in the workplace include education, treating potential therapeutic targets and ensuring protection in the working environment. The consideration of a compensation scheme, for families of healthcare workers that have suffered because of COVID-19, would go some way to support the recovery process.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".