Reopening schools safely in the face of COVID-19: Can cluster randomized trials help?
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted the challenges of evidence-based health policymaking, as critical precautionary decisions, such as school closures, had to be made urgently on the basis of little evidence. As primary and secondary schools once again close in the face of surging infections, there is an opportunity to rigorously study their reopening. School-aged children appear to be less affected by COVID-19 than adults, yet schools may drive community transmission of the virus. Given the impact of school closures on both education and the economy, schools cannot remain closed indefinitely. But when and how can they be reopened safely? We argue that a cluster randomized trial is a rigorous and ethical way to resolve these uncertainties. We discuss key scientific, ethical, and resource considerations both to inform trial design of school reopenings and to prompt discussion of the merits and feasibility of conducting such a trial.
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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 | 0.457 | 0.570 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.016 | 0.013 |
| 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".