Basics of Developing a COVID-19 Reopening Roadmap: A Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: The necessity of easing pandemic restrictions is explicit. Due to the harsh consequences of lockdowns, governments are willing to find reasonable pathways to reopen their activities. METHODS: July 2020, we conducted a systematic search on PubMed, Scopus, and Web of Science to review the databases; and Google by manual to review the grey literature. Two independent authors extracted the data, and the senior author solved the discrepancies. RESULTS: Sixteen documents were included. Data categorized into four sections: principals, general recommendations for individuals, health key metrics, and in-phases strategy. The number of phases or stages differed from three to six, with a minimum of two weeks considered for each one. Health key metrics were categorized into four subsets: sufficient preventive capacities, appropriate diagnostic capacity, appropriate epidemiological monitoring, and sufficient health system capacity. These metrics were used as the criteria for progressing or returning over the roadmap, which guarantees a roadmap's dynamicity. Noticeably, few roadmaps did not mention the criteria that may alter the dynamicity of their roadmap. When some areas face new surges, the roadmap's dynamicity is essential, and it is vital to describe the criteria to stop the reopening process and implement the restrictions again. CONCLUSION: Providing evidence for policymaking about lifting the COVID-19 restrictions seems to be missed in the literature should be addressed more, and further studies are recommended.
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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 | 0.026 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".