The First Three Months of the COVID-19 Pandemic: The World Health Organization's Response
Bibliographic record
Abstract
Abstract Infectious diseases are generally preventable public health problems; however, they cause epidemics, economic losses, and deaths at all levels worldwide. Especially with the effect of globalization, it has been observed much better with the COVID-19 pandemic, where viral outbreaks can rapidly affect all stages of life. Experiences show that not only national authorities, but also international organizations should play critical roles in the control of infectious diseases. Based on this perspective, in our study, the responses of the World Health Organization (WHO), which has a decisive role in international health policies, to prevent the spread of the COVID-19 pandemic in the first three-month period were examined. In addition, the possible effects of the national measures and policies taken by Canada, Turkey, Pakistan, and Tajikistan, which are in different income groups according to the World Bank, on the number of cases and deaths were discussed. In this context, the contributions, or potential effects of the WHO on the performance of these countries against the COVID-19 were interpreted. It was understood that the countries in different income groups have developed policies based on their capability at country level independent from other countries in the fight against COVID-19 outbreak. It has been evaluated that the success of countries in the fight against the outbreaks is affected by different parameters such as average age of the country's populations, health infrastructures and income levels that greatly affect access to medical supplies and equipment. However, it can be said that international organizations with institutional power such as WHO cannot play a leading role at the expected level in the prevention of the pandemic and ineffective in directing local policies and ensuring international coordination. Especially in the COVID-19 viral pandemic environment where fast and precise decisions are required, this leading role is desperately needed. The first quarterly decisions and meeting notifications of the WHO in the COVID-19 pandemic were examined in our study and it was understood that these outcomes cannot provide a definite and effective management plan to national health policy makers, although WHO presented diagnostic tools rapidly and, in some degree maintained the supply chain of medical equipment globally. As a result, when all these elements determined in our study were considered together; the need for an integrative, proactive, and fair international health institution that produces prompt, effective and constantly updated comprehensive policies should be emphasized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".