Diverging levels of COVID-19 governmental response satisfaction across middle eastern Arab countries: a multinational study
Bibliographic record
Abstract
BACKGROUND: Public acceptance of governmental measures are key to controlling the spread of infectious diseases. The COVID-19 pandemic has placed a significant burden on healthcare systems for high-income countries as well as low- and middle-income countries (LMICs). The ability of LMICs to respond to the challenge of the COVID-19 pandemic has been limited and may have affected the impact of governmental strategies to control the spread of COVID-19. This study aimed to evaluate and compare public opinion on the governmental COVID-19 response of high and LMICs in the Middle East and benchmark it to international countries. METHODS: An online, self-administered questionnaire was distributed among different Middle Eastern Arab countries. Participants' demographics and level of satisfaction with governmental responses to COVID-19 were analyzed and reported. Scores were benchmarked against 19 international values. RESULTS: A total of 7395 responses were included. Bahrain scored highest for satisfaction with the governmental response with 38.29 ± 2.93 on a scale of 40, followed by the Kingdom of Saudi Arabia (37.13 ± 3.27), United Arab Emirates (36.56 ± 3.44), Kuwait (35.74 ± 4.85), Jordan (23.08 ± 6.41), and Lebanon (15.39 ± 5.28). Participants' country of residence was a significant predictor of the satisfaction score (P < 0.001), and participants who suffered income reduction due to the pandemic, had a history of SARS-CoV-2 infection, and held higher educational degrees had significantly lower satisfaction scores (P < 0.001). When benchmarked with other international publics, countries from the Gulf Cooperation Council had the highest satisfaction level, Jordan had an average score, and Lebanon had one of the lowest satisfaction scores. CONCLUSION: The political crisis in Lebanon merged with the existing corruption were associated with the lowest public satisfaction score whereas the economical instability of Jordan placed the country just before the lowest position. On the other hand, the solid economy plus good planning and public trust in the government placed the other countries of the Gulf Cooperation Council on top of the scale. Further investigation is necessary to find out how the governments of other low-income countries may have handled the situation wisely and gained the trust of their publics. This may help convey a clearer picture to Arab governments that have suffered during the 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".