Effects of Closures and Openings on Public Health in the Time of COVID-19: A Cross-Country and Temporal Trend Analysis
Bibliographic record
Abstract
Many countries mandated social distancing measures during the COVID-19 pandemic of 2020 to 2022 that variously included opening hours restrictions on hospitality and retail, economy-wide closures, and additional international border controls. We analyzed whether more restrictive (hereafter, closures) or less restrictive (hereafter, openings) social distancing measures changed the short-term trends in the number of COVID-19 cases, hospitalizations, and ICU patients in Australia, Canada, and the United Kingdom. Our analysis uses a “before-and-after” trend analysis (decremental/incremental and growth/decay trends) to compare the trends of epidemic indicators before and after each closure or opening event. Results show that, in general, and for these three countries: (a) closures resulted in reduced trend growth in adverse COVID-19 public health outcomes and (b) openings resulted in increased trend growth for the three selected measures of public health.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".