A lesson learned from Middle East respiratory syndrome (MERS) in Saudi Arabia
Bibliographic record
Abstract
Middle East respiratory syndrome (MERS) caused by novel Corona virus hit Kingdom of Saudi Arabia (KSA) and resulted in hundreds of mortality and morbidity, fears and psychosocial stress among population, economic loss and major political change at Ministry of Health (MoH). Although MERS discovered two years ago, confusion still exists about its origin, nature, and consequences. In 2003, similar virus (SARS) hit Canada and resulted in a reform of Canada's public health system and creation of a Canadian Agency for Public Health, similar to the US Centers for Disease Control (CDC). The idea of Saudi CDC is attractive and even "sexy" but it is not the best option. Experience and literature indicate that the best option for KSA is to revitalize national public health systems on the basis of comprehensive, continuing, and integrated primary health care (PHC) and public health (PH). This article proposes three initial, but essential, steps for such revitalization to take place: political will and support, integration of PHC and PH, and on-job professional programs for the workforce. In addition, current academic and training programs for PHC and PH should be revisited in the light of national vision and strategy that aim for high quality products that protect and promote healthy nation. Scientific associations, medical education research chair, and relevant academic bodies should be involved in the revitalization to ensure quality of process and outcomes.
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 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 | 0.004 | 0.004 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".