Knowledge and Beliefs towards Universal Safety Precautions to flatten the Curve during Coronavirus Disease (COVID-19) Pandemic among the General Public in India: Explorations from a National Perspective (Preprint)
Bibliographic record
Abstract
BACKGROUND Background: The novel Coronavirus disease (COVID-19) is being considered as the most serious health threat that the world has never witnessed in the recent times and significantly affecting the daily routine of mankind by emerging as a global pandemic. As there is no treatment nor a vaccine that was approved so far, universal safety precautions (USPs) and mitigating strategies are the only way to deal with this emergency crisis. However, studies on knowledge and beliefs towards USPs among the general public in countries such as India with a large population are lacking. OBJECTIVE Objective: The goal of this study is to assess the knowledge and beliefs towards universal safety precautions during COVID-19 pandemic among the general public in India. METHODS Methods: A cross-sectional, web-based online survey was conducted among the general public in India during March 2020. A 20-item validated, a self-administered survey questionnaire was developed and randomly distributed among the public using Google Forms through social media networks. Descriptive statistics were used in representing the study characteristics, and the Chi-square test was used in assessing the associations among the study variables with a p-value of < 0.05 was considered as statistically significant. RESULTS Results: Of 1287 participants, 1117 have given their consent of willingness and completed the questionnaire with a response rate of 86.8%. The mean age of the study participants was 28.8 ± 10.9 years, and based upon the socio-demographic information, the majority were post-graduates (32.9%), professional job holders (45%) and belonged to the upper-middle (40%) economic class. Overall, the average correct responses of knowledge (~63%) and beliefs (~83%) towards USPs and mitigating strategies among participants varied between moderate to high, with statistically significant associations with their socio-demographic characteristics. CONCLUSIONS Conclusions: Although the knowledge and beliefs of the general public in India towards USPs are encouraging, there is a need for long-term educational interventions as the dynamics and severity of COVID-19 have been changing day-by-day rapidly. The findings of this study could guide the public health authorities in making and implementing decisions to combat this pandemic. CLINICALTRIAL N/A
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".