Disproportionate infection, hospitalisation and death from COVID-19 in ethnic minority groups and Indigenous Peoples: an application of the Priority Public Health Conditions analytical framework
Bibliographic record
Abstract
The COVID-19 pandemic has resulted in disproportionate consequences for ethnic minority groups and Indigenous Peoples. We present an application of the Priority Public Health Conditions (PPHC) framework from the World Health Organisation (WHO), to explicitly address COVID-19 and other respiratory viruses of pandemic potential. This application is supported by evidence that ethnic minority groups were more likely to be infected, implying differential exposure (PPHC level two), be more vulnerable to severe disease once infected (PPHC level three) and have poorer health outcomes following infection (PPHC level four). These inequities are driven by various interconnected dimensions of racism, that compounds with socioeconomic context and position (PPHC level one). We show that, for respiratory viruses, it is important to stratify levels of the PPHC framework by infection status and by societal, community, and individual factors to develop optimal interventions to reduce inequity from COVID-19 and future infectious diseases outbreaks.
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How this classification was reachedexpand
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".