Do in-person nationwide university entrance exams affect COVID-19 transmission? An experience from Iran
Bibliographic record
Abstract
BACKGROUND: Nationwide University Entrance Exams (NUEEs) are essential scheduled exams that, in some countries, have been conducted in-person even during the COVID-19 pandemic. Considering the potentially fatal consequences of any pandemic-related decisions by policymakers, the response of the national health systems and regulatory bodies to this matter must be evidence-based. This study has been the first to evaluate the effects of NUEEs on measurements and indices associated with COVID-19. METHODS: Five NUEEs were conducted in the year 2020 from July 30 to August 22 in the province of Fars, located in the south of Iran. The trends of emergency services, 16-hour special health center visits, PCR tests, hospital services, and death rates due to COVID-19 following these NUEEs were assessed in a 72-day span in this study. RESULTS: All COVID-19 related indices and measurements in this study showed a decreasing trend across the board with the exception of the number of total and positive polymerase chain reaction ( PCR) tests. CONCLUSION: This study found that following the conduction of 2020 NUEEs, there was no increasing trend in the number of COVID-19 cases and the associated indices and measurements. The findings of this study will be valuable for future practices, health protocols, and policies. This study also highlighted the importance of the constant implementation of strict health protocols and measures before, during, and after each NUEE.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".