Basics of developing a COVID-19 reopening roadmap, a systematic scoping review of reopening roadmaps
Bibliographic record
Abstract
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: To find out the basics of developing a reopening roadmap, on 6th-10th August 2020, we conducted a systematic search on Pubmed, Scopus, Embase, and Web of Science, but no roadmap was found. Then, we manually searched Google to review the grey literature. Two independent authors extracted the data, and the senior author solved the discrepancies.Results: According to the search strategy, finally, sixteen roadmaps 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 diagnosis capacity, appropriate epidemiological monitoring capacity, 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 didn’t mention these 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. Conclusions: 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 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 | 0.172 | 0.351 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.052 | 0.031 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".