Roadmap to recovery: Implemented and attitude toward school reopening strategies during the COVID-19 pandemic, a scoping review
Bibliographic record
Abstract
The novel coronavirus disease 2019 (COVID-19) has had various financial and life impacts on the world's population. Schools' regular activity and function during the pandemic require balancing the repercussions of suspending in-person education versus health threats. Furthermore, children are one of the prominent victims of the restricted quarantine strategies' effects, which may make them vulnerable to various mental health problems. In this study, we reviewed previously reported strategies and roadmaps regarding the reopening of schools during the COVID-19 pandemic. The following databases were searched from October to December 2021, via multi-step search strategies for "COVID-19," "coronavirus," "school reopening," "roadmaps," "reopening," and "reopening strategies": Google Scholar, PubMed, Scopus, and Web of Science. A total of five papers with roadmaps focusing on reopening schools were included in this study. Fundamental issues and principles of these reviewed roadmaps were: 1) protecting the high-risk students and staff physically and mentally, 2) accelerating the vaccination of essential workers, staff, parents, and students, and 3) improving the COVID-19 testing capacity. Roadmaps for the reopening of the schools should describe some phases and steps for their strategies. Current roadmaps have not mentioned any phases and timelines for this process. Describing some health metrics in the roadmaps for progressing to the next step or returning to the previous ones is also necessary for all roadmaps and should be considered in further studies.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".