Ensuring adequate health financing to prevent and control the COVID-19 in Iran
Bibliographic record
Abstract
2020, the Iranian Ministry of Health and Medical Education (MoHME) has announced the first 2 cases of SARS-CoV-2, a novel emerging coronavirus which causes an infection termed as COVID-19, in Qom city. As such, the Iranian government, through the establishment of the "National Headquarters for the management and control of the novel Coronavirus", has started implementing policies and programs for the prevention and control of the virus. These measures include schools and universities closure, reduced working hours, and increased production and delivery of equipment such as masks, gloves and hygienic materials for sterile environments. The government has also made efforts to divulge high-quality information concerning the COVID-19 and to provide laboratories and hospitals with diagnostic kits and adequate resources to treat patients. However, despite such efforts, the number of cases and deaths has progressively increased with rising trends in total confirmed cases and deaths, as well as in new daily cases and deaths associated with the COVID-19. Iran is a developing country and its economic infrastructure has been hit hardly by embargo and sanctions. While developed countries have allocated appropriate funding and are responding adequately to the COVID-19 pandemics, Iran has experienced a serious surge of cases and deaths and should strive to provide additional resources to the health system to make healthcare services more accessible and to increase the fairness of that access. All relevant actors and stakeholders should work together to fight this disease.
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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 | 0.007 | 0.009 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".