Public perspective on the governmental response, communication and trust in the governmental decisions in mitigating COVID-19 early in the pandemic across the G7 countries
Bibliographic record
Abstract
The COVID-19 pandemic poses a threat to global health and security inciting governments with the responsibility to respond with measures that ensure the health and safety of their communities. We assessed public attitudes towards governmental actions to combat the COVID-19 pandemic in the G7 countries. Data were collected during 19th-21st March 2020, from 7005 Kantar's online panelists aged >16 years across the G7 countries: Canada, France, Great Britain, Germany, Italy, Japan, and the United States. Data were post-stratified and weighted to match population distributions of the respective countries. Descriptive and multivariable analyses were conducted. Amongst the G7, Japan had the lowest level of approval of governmental response to the pandemic, rating governmental communication as good, and trusting governmental decisions (35.0%, 33.6%, and 38.0%, respectively), followed by the U.S. (52.9%, 64.6%, and 59.9%, respectively). Understanding of which measures one can personally take to help limit the spread of the coronavirus was significantly associated with approving governmental response (aOR = 2.88), rating government communication as good (aOR = 2.70) and trust in future governmental decisions (aOR = 2.73). Those who reported government/politicians and friends/family as their most trusted information source were more likely to report approval, higher rating, and/or trust toward governmental actions. Public attitudes towards governmental actions against COVID-19 varied substantially across the G7 countries and were associated with the understanding of measures and source of information that respondents most trusted. Timely and accurate communication is essential to enhance public engagement to control the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".