Social trust and COVID-19 mortality in the United States: lessons in planning for future pandemics using data from the general social survey
Bibliographic record
Abstract
Abstract Background The United States has lost many lives to COVID-19. The role of social capital and collective action has been previously explored in the context of COVID-19. The current study specifically investigates the role of social trust at the county level and COVID-19 mortality in the US, hypothesizing that counties with higher social trust will have lower COVID-19 mortality rates. Methods We used cross-sectional data from the General Social Survey (GSS). We collected COVID-19 mortality data from the COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University until October 31, 2021. We obtained county characteristics from the 2019 American Community Survey and supplemented this data source with additional publicly available county-level data, such as measures of income inequality and political leanings. We measured social trust as a single item from the GSS and calculated mean social trust in a county by pooling responses from 2002 to 2018. We then modeled the relationship between mean social trust and COVID-19 mortality. Results Results indicate that counties with higher social trust have lower COVID-19 mortality rates. Higher values of mean social trust at the county level are associated with a decrease in COVID-19 mortality (b= -0.25, p-value < 0.001), after adjustment for confounding. The direction of association is consistent in a sensitivity analysis. Conclusions Our findings underscore the importance of investment in social capital and social trust. We believe these findings can be applied beyond the COVID-19 pandemic, as they demonstrate the potential for social trust as a method for emergency preparedness.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".