The Social Lives of Infectious Diseases: Why Culture Matters to COVID-19
Bibliographic record
Abstract
Over the course of the year 2020, the global scientific community dedicated considerable effort to understanding COVID-19. In this review, we discuss some of the findings accumulated between the onset of the pandemic and the end of 2020, and argue that although COVID-19 is clearly a biological disease tied to a specific virus, the culture-mind relation at the heart of cultural psychology is nonetheless essential to understanding the pandemic. Striking differences have been observed in terms of relative mortality, transmission rates, behavioral responses, official policies, compliance with authorities, and even the extent to which beliefs about COVID-19 have been politicized across different societies and groups. Moreover, many minority groups have very different experiences of the pandemic relative to dominant groups, notably through existing health inequities as well as discrimination and marginalization, which we believe calls for a better integration of political and socioeconomic factors into cultural psychology and into the narrative of health and illness in psychological science more broadly. Finally, individual differences in, for example, intolerance of uncertainty, optimism, conspiratorial thinking, or collectivist orientation are influenced by cultural context, with implications for behaviors that are relevant to the spread and impact of COVID-19, such as mask-wearing and social distancing. The interplay between cultural context and the experience and expression of mental disorders continues to be documented by cultural-clinical psychology; the current work extends this thinking to infectious disease, with special attention to diseases spread by social contact and fought at least in part through social interventions. We will discuss cultural influences on the transmission, course, and outcome of COVID-19 at three levels: (1) cross-society differences; (2) within-society communities and intergroup relations; and (3) individual differences shaped by cultural context. We conclude by considering potential theoretical implications of this perspective on infectious disease for cultural psychology and related disciplines, as well as practical implications of this perspective on science communication and public health interventions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".