Knowledge, Attitude and Practice towards COVID-19 Vaccination among adults of Sullia Taluk in Dakshin Kannada District of Karnataka- A Community based Survey)
Bibliographic record
Abstract
Background: Vaccination programs for corona virus disease (COVID-19) were initiated globally in a record time unparalleled in the history of immunisation. Thus the community's and perceptions towards COVID-19 vaccinations are poorly understood. This study thus aimed to investigate community knowledge, attitudes and practices towards COVID-19 vaccinations in Sullia Taluk of Dakshin Kannada. Methods: An exploratory and anonymous population- based survey was conducted among 600 general individuals (58.17% male; 41.83% female). The survey was conducted using a validated self- administered questionnaire containing a set of questions pertaining to knowledge, attitudes, and practices. Multiple linear regression was performed to determine the variables predicting knowledge, and attitudes towards COVID-19 vaccinations. Results: The mean scores of knowledges and attitudes were 2.73±1.48 and 9.44±2.39 respectively. About a quarter of participants thought that the COVID-19 vaccination available in India is safe, 60% reported that they will continue to have further vaccinations if necessary. About 54% reported recommending it to family and friends. Regression analysis revealed that higher SES, university/ higher levels of education, nuclear families and those with a previous history of essential vaccines uptake were associated with a higher knowledge score; whilst attitudes were significantly associated to gender and previous history of essential vaccines uptake. Just over half of the participants(54%) thought that everyone should be vaccinated. A majority of the population 72.17% population reported vaccine should be administered free of cost in India. Keywords:- Covid Vaccination; Survey; Karnatak.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".