Heterogeneous impacts of and vulnerabilities to the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has impacted all sectors of society, with effects that have been acutely experienced at the local, national, regional, and global levels. This study examined the heterogeneous impacts of and vulnerability to COVID-19 for promoting urban sustainability and resilience. We performed a scoping review on the basis of the relevant literature from the Web of Science and PubMed, and a national survey conducted among a total of 5,376 participants in early 2020. The survey adopted a repeated cross-sectional design to study changes in residents’ risk perception of COVID-19 across the three stages (21–23 January, 27–28 February, and 24–27 March), using a snowball sampling method to recruit 2,144, 2,021, and 1,211 participants, respectively. This study revealed that the spatial, social, economic, and health impacts of COVID-19 have not been distributed evenly among populations, with specific individuals and communities more vulnerable than others. Among the determinants of these inequalities are socioeconomic status, housing arrangements, and working requirements, which influence the extent to which people can safely adhere to stay-at-home and social distancing policies and how they perceive risks. Additionally, racial/ethnic minorities face differing risks, in part because of socioeconomic factors but also because some groups experience higher shares of comorbidities. Moreover, overall, these risk factors are the healthcare systems meant to shield individuals and communities from pandemic impacts, which, however, have become increasingly taxed due to the sudden influx of patients and the resultant shortages of resources – including crucial personal protective equipment to minimize interpersonal transmission. Understanding the heterogeneous impacts of and vulnerability to COVID-19 could inform the design of environmentally sustainable and socially resilient cities, making them better equipped to encounter future epidemics. This study would help us identify more effective and equitable solutions to the ongoing challenges of the pandemic, promoting sustainability and resilience at multiple societal levels.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".