The Impact of COVID-19 Vaccine Communication, Acceptance, and Practices (CO-VIN-CAP) on Vaccine Hesitancy in an Indian Setting: Protocol for a Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 vaccine is considered to be a key to limiting and eliminating infectious disease. But, the success of the vaccination program will rely on the rates of vaccine acceptance among the population. OBJECTIVE: The objective of this study is to examine the factors that influence vaccine hesitancy and vaccine acceptance and to explore the unintended consequences of COVID-19 infections. The study will further explore the association between socio-demographic characteristics, health status, COVID-19 related knowledge, attitude, practice, and its influence on Vaccine hesitancy and acceptance among individuals living in urban and rural settings of Chennai, Tamil Nadu in the Southern state of India. METHODS: A cross-sectional study will be conducted between January 2021 and January 2023. A sample of approximately 25,000 individuals will be recruited and enrolled using a non-probability complete enumeration sampling method from eleven selected urban and rural settings of Chennai. The data will be collected at a one-time point by administering the questionnaire to the eligible study participants. The collected data will be used to assess the rates of vaccine acceptance, hesitancy as well as knowledge, attitudes, practices, and beliefs regarding COVID-19 and COVID-19 vaccine. Lastly, the study questionnaire will be used to assess the unintended consequences of COVID-19 infection. RESULTS: A pilot of 2500 individuals has been conducted to pre-test the self-administered study questionnaire. The data collection initiated on March 1, 2021 and the initial results are planned for publication by June 2021. Descriptive analysis of the gathered data will be performed using Statistical Analysis System (SAS) v9.1 and reporting of the results will be done at 95% confidence interval and P=.049. The study will help explore the burden of vaccine acceptance and hesitancy among individuals living in urban and rural settings of Chennai. Further, it will help to examine the variables that influence vaccine acceptance and hesitancy. Lastly, the result findings will help to design and develop a user-centered informatics platform that can deliver multimedia-driven health educational modules tailored to facilitate vaccine uptake in varied settings. CONCLUSIONS: The proposed study will help in understanding the rate and determinants of COVID-19 vaccine acceptance and hesitancy among the population of Chennai. The findings of the study would further facilitate the development of a multifaceted intervention to enhance vaccine acceptance among the population.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".