Public Perceptions Regarding the Preparedness of Government to Combat the Third Wave of COVID-19 (SARS-CoV-2) Infection Across Various States of India
Bibliographic record
Abstract
India has already passed through 2 waves of the coronavirus disease (COVID-19) pandemic losing many lives. The reason for losing lives may be due to the unpreparedness of the health care system of India for this unprecedented pandemic. To assess the government's preparedness, an institutional-based cross-sectional prospective survey was conducted among the adult population of selected states in India. A self-administered 30-item questionnaire divided into 5 sections (demography of the participants, steps to create awareness, prevent spread of infection, handle the emergency, and prognosis) was distributed online through Google Forms. The responses were collected in an Excel file. SPSS software was used to perform the descriptive statistics and analysis of variance (ANOVA). Nearly a quarter of the participants "strongly disagree"/"disagree" about the government's preparedness for the third wave. Considering their perception, it cannot be assured that the government is well prepared to handle the emergency. So, the government must maintain emergency funding and develop a health infrastructure. The government should take steps to reduce social stigma, prevent spreading of unscientific propagation, and make people aware of the World Health Organization (WHO) as the reliable source of information for health emergencies to avoid a human crisis in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".