Coverage of Coronavirus Disease-2019 (COVID-19) Booster Dose (Precautionary) in the Adult Population: An Online Survey
Bibliographic record
Abstract
Background The coronavirus disease-2019 (COVID-19) pandemic devastated public health worldwide, including India. COVID-19 vaccines and their boosters are life-saving developments that have helped prevent and control the spread of COVID-19. We conducted this study to assess the coverage of the booster dose in an Indian population (the third dose of the COVID-19 vaccine in India is referred to as the booster or precautionary dose), record the reasons for not taking the booster dose, and determine the effectiveness of the booster. The levels of adherence to COVID-19 precautionary behavior was also assessed. Methods We conducted a descriptive, cross-sectional study using convenient sampling via an online survey of 550 respondents older than 18 in the second quarter of 2022. The respondents were distributed among 18 states and union territories in India. The data were analyzed as simple proportions and percentages. Results Of the 550 respondents, 152 (27.6%) received the booster dose, indicating low coverage. A small percentage of respondents (7.2%) reported suffering from COVID-19 following the booster, of whom 91% were medical professionals. The most common reported reason for not taking the vaccine was that the respondents were not yet due for their dose (48.1%). The time between the second dose of the COVID-19 vaccine and the booster had no impact on infection rates. Men were less likely to adhere to COVID-19 precautionary behavior than women, despite similar vaccination rates. Conclusion The COVID-19 vaccine booster had a low acceptance in our study population, with roughly one-quarter of the population receiving the booster. The booster dose has been influential in the prevention of COVID-19. Most respondents followed behavioral safety measures despite the decline of active cases of COVID-19 in India following the Omicron wave. Our results indicate a need to strengthen public strategies to affect behavioral changes, such as improving India's Behavior Change Communication program to ensure adequate booster dose coverage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".