Public satisfaction with COVID-19 policy responses and their implementation: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has prompted significant changes in health policies worldwide. Policy-makers from various countries have responded by adopting and implementing diverse policy measures aimed at combating the spread and impact of COVID-19. The aim of this study is to assess people's satisfaction with the primary policy responses and their perceptions of the success of their implementation and monitoring. METHODS: A cross-sectional online survey was conducted in Kerman, Iran, spanning the period of 2021-2022. The sample included adults aged 18 years and older who had access to the Internet and smartphone devices. An online platform was used to develop the questionnaire and collect the data. The face validity, comprehensibility and content validity of the questionnaire were tested and met. Descriptive statistics and multivariable logistic regression were conducted. Data were analyzed using STATA 14.0 software. RESULTS: In total, 3192 participants completed the questionnaire, resulting in a completion rate of 67%. More than half of the participants were female (55.51%), with a mean age of 37 ± 11.72 years, and the majority held an academic degree (74.97%). Overall, 54.79% of participants expressed satisfaction with the adopted policy responses, while 56.61% were dissatisfied with their implementation and monitoring. In multivariable logistic regression, factors positively associated with satisfaction included having a diploma [adjusted odds ratio (AOR) = 1.46; 95% confidence interval (CI) 1.05-2.04], an academic degree (AOR = 1.71; 95% CI 1.26-2.31) and middle socioeconomic status (AOR = 1.34; 95% CI 1.07-1.69). In contrast, being male (AOR = 0.68; 95% CI 0.58-0.79) and having high trust in others (AOR = 0.75; 95% CI 0.61-0.92) were associated with lower odds of satisfaction. CONCLUSIONS: The results of the study showed that more than half of the participants expressed satisfaction with the adopted policy responses made by the National Committee to Combat COVID-19. However, it seems that the government has performed poorly in implementing and monitoring adopted policy responses, leading to a decrease in people's satisfaction.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".