Impact of school closures and re-openings on COVID-19 transmission
Bibliographic record
Abstract
BACKGROUND: Globally, the education of students at primary and secondary schools has been severely disrupted by the implementation of school closures to reduce the spread of coronavirus disease 2019 (COVID-19). The effectiveness of school closures in reducing transmission of COVID-19 and the impact of re-opening schools are unclear. METHODS: Research criteria for this rapid review included empirical studies, published or pre-published worldwide before January 25, 2021, that assessed the effectiveness of school closures in reducing the spread of COVID-19 and the impact of school re-openings on COVID-19 transmission. RESULTS: Twenty-four studies on the impact of school closures and re-openings on COVID-19 transmission were identified through the seven databases that were searched. Overall the evidence from these studies was mixed and varied due to several factors such as the time of implementation of public health measures, research design of included studies and variability among the levels of schooling examined. CONCLUSION: Preliminary findings suggest that school closures have limited impact on reducing COVID-19 transmission, with other non-pharmaceutical interventions considered much more effective. However, due to the limitations of the studies, further research is needed to support the use of this public health measure in response to the COVID-19 pandemic.
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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 | 0.023 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".