Public Health Strategies for the Gradual Lifting of the Public Sector Lockdown in Jordan and the United Arab Emirates During the COVID-19 Crisis
Bibliographic record
Abstract
In this viewpoint, we present public policies and public health strategies for a gradual lockdown lifting during the coronavirus disease (COVID-19) crisis in two country cases, Jordan and the United Arab Emirates. While managing pandemics is critical in terms of preparedness, response, and recovery, it is equally vital to ensure that the measures for a lockdown exit are both efficient and effective. It is critical to learn from first-wave lessons to systematize responses during times of crisis and execute appropriate public policies and public health strategies. This viewpoint highlights the importance of the following during lockdown lifting: pandemic control, health care capacity, training, scaling up of resources and systems, and priority setting of public policies by acknowledging challenges, developing policy insights, and setting the policy direction. The systematic approaches and leadership thinking required for lifting lockdowns during a crisis include the three Rs: Readiness, Responses, and Resilience & Recovery.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".