POLICY OF PROMOTING ALCOHOL CONSUMPTION IN TIMES OF COVID-19 LOCKDOWN IN INDIA: A MEDICAL AND CONSTITUTIONAL ANALYSIS.
Bibliographic record
Abstract
BACKGROUND: India, with the declaration of COVID-19 as a pandemic, started imposing restrictions in the country th and initiated a nationwide lockdown under Section 6 of the Disaster Management Act, 2005 on 24 March 2020, followed by four phases of lockdown and then gradual unlock of the country. The rationale behind the same was to avoid social contact. Alcohol dispensing was also stopped during this time and was among the rst services to be reopened by the States. We propose in this paper that this lifting of ban on alcohol sale during the pandemic has led to a signicant increase in the number of COVID-19 cases in the country. METHODS: This is a prospective, observational study, done by collecting data from the Aargya Setu App, which is a mobile application launched by the Ministry of Health and Family Welfare on 2 April 2020 for contact tracing and elf assessment of COVID-19. The data of cumulative number of cases in 12 selected states of the country were compared before and after the lift of ban of alcohol and signicance was shown by the paired t test. RESULTS: The number of COVID-19 positive cases in the country during nationwide lockdown with simultaneous ban on alcohol sale when compared to cumulative number of cases after the lift of ban of alcohol sale during Lockdown and initial Unlock is statistically signicant (p = 0.04) CONCLUSION: We found that the decision to restart the sale of Alcohol could have been a factor for rise in number of cases in the country in the given timeframe. The decision to start the sale has also not been in accordance with the Indian Constitution and against the nation's founding ethics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".