Ensuring adequate health financing to prevent and control the COVID-19 in Iran
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it