COVID-19 Vaccine Knowledge, Attitude, and Acceptance in Students of Tertiary Institutions in Central Nigeria
Bibliographic record
Abstract
INTRODUCTION/BACKGROUND: On March 11, 2020, the World Health Organization declared the COVID-19 outbreak a global pandemic. Nigeria, among African nations, has borne the highest burden of COVID-19 reporting 163,498 cases and 2,058 fatalities. Institutions of higher learning possess certain characteristics that can increase the risk of COVID-19 transmission within their campuses. These features include a sizable student population, high population density, and frequent student interactions. As a result, it is imperative to implement protective measures to mitigate the virus’s spread on campus. AIM/OBJECTIVE: This research aimed to explore the connection between the knowledge, attitudes, and acceptance of COVID-19 vaccines among students in tertiary institutions located in Central Nigeria. METHODOLOGY: An anonymous online survey was conducted among Nigerian students, gathering information related to their demographics, as well as assessing their knowledge, attitudes, and willingness to accept vaccines in the post-COVID-19 era. The collected data were subjected to analysis through descriptive and inferential statistics. RESULTS: Out of the 400 participants included in the survey, 140 (35.0%) reported having already received a COVID-19 vaccine, while 144 (36.0%) expressed an intention to be vaccinated. The analysis indicated that there is a positive yet very weak correlation between attitudes towards COVID-19 vaccination and the intention to get vaccinated (r = −0.023, N = 365, p < 0.01). Conversely, knowledge regarding COVID-19 vaccines demonstrated a significant positive correlation with the intent to be vaccinated (r = 0.222, N = 367, p < 0.01). CONCLUSION: In conclusion, this study underscores the importance of students’ knowledge and attitudes regarding vaccines in shaping their acceptance of COVID-19 vaccines. The results emphasize the critical necessity of providing comprehensive information on COVID-19 vaccines to address concerns related to unforeseen side effects, mitigate general mistrust in vaccine benefits, and alleviate apprehensions about the profitability of pharmaceutical companies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".