Socioeconomic Status and Adherence to Preventive Measures During the COVID-19 Pandemic in Switzerland: A Population Based Digital Cohort Analysis
Bibliographic record
Abstract
Objectives: To assess the association between socioeconomic status (SES) and self-reported adherence to preventive measures in Switzerland during the COVID-19 pandemic. Methods: 4,299 participants from a digital cohort were followed between September 2020 and November 2021. Baseline equivalised disposable income and education were used as SES proxies. Adherence was assessed over time. We investigated the association between SES and adherence using multivariable mixed logistic regression, stratifying by age (below/above 65 years) and two periods (before/after June 2021, to account for changes in vaccine coverage and epidemiological situation). Results: Adherence was high across all SES strata before June 2021. After, participants with higher equivalised disposable income were less likely to adhere to preventive measures compared to participants in the first (low) quartile [second (Adj.OR, 95% CI) (0.56, 0.37-0.85), third (0.38, 0.23-0.64), fourth (0.60, 0.36-0.98)]. We observed similar results for education. Conclusion: No differences by SES were found during the period with high SARS-CoV-2 incidence rates and stringent measures. Following the broad availability of vaccines, lower incidence, and eased measures, differences by SES started to emerge. Our study highlights the need for contextual interpretation when assessing SES impact on adherence to preventive measures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".