A cross-sectional study of the association of age, gender, education and economic status with individual perceptions of governmental response to COVID-19
Bibliographic record
Abstract
OBJECTIVE: We assessed the impact of key population variables (age, gender, income and education) on perceptions of governmental effectiveness in communicating about COVID-19, helping meet needs for food and shelter, providing physical and mental healthcare services, and allocating dedicated resources to vulnerable populations. DESIGN: Cross-sectional study carried out in June 2020. PARTICIPANTS AND SETTING: 13 426 individuals from 19 countries. RESULTS: More than 60% of all respondents felt their government had communicated adequately during the pandemic. National variances ranged from 83.4% in China down to 37.2% in Brazil, but overall, males and those with a higher income were more likely to rate government communications highly. Almost half (48.8%) of the respondents felt their government had ensured adequate access to physical health services (ranging from 89.3% for Singapore to 27.2% for Poland), with higher ratings reported by younger and higher-income respondents. Ratings of mental health support were lower overall (32.9%, ranging from 74.8% in China to around 15% in Brazil and Sweden), but highest among younger respondents. Providing support for basic necessities of food and housing was rated highest overall in China (79%) and lowest in Ecuador (14.6%), with higher ratings reported by younger, higher-income and better-educated respondents across all countries. The same three demographic groups tended to rate their country's support to vulnerable groups more highly than other respondents, with national scores ranging from around 75% (Singapore and China) to 19.5% (Sweden). Subgroup findings are mostly independent of intercountry variations with 15% of variation being due to intercountry differences. CONCLUSIONS: The tendency of younger, better-paid and better-educated respondents to rate their country's response to the pandemic more highly, suggests that government responses must be nuanced and pay greater attention to the needs of less-advantaged citizens as they continue to address this pandemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".