Perceptions and Uptake of COVID-19 Vaccines amongst Undergraduate Students in a Tertiary Institution in Lagos State
Bibliographic record
Abstract
Background: COVID-19 pandemic has ravaged the world, causing deaths in different countries. Fortunately, production of its vaccine has brought some tranquillity, and Nigeria was not left behind. This study aimed to determine the role of knowledge and perception towards the uptake of COVID-19 vaccine amidst undergraduate students of the University of Lagos, Lagos, Nigeria. Methods: This descriptive cross-sectional study was carried out amongst 170 students at the University of Lagos using a multi-stage sampling method. Self-administered questionnaires were used to collect information on demography, knowledge, perception, acceptance and uptake of COVID-19 vaccine. Data were analysed utilising SPSS Version 26. The level of significance was at P < 0.05. Results: Majority of respondents 125 (73.5%) had good knowledge of COVID-19 vaccine and 87 (51.2%) respondents attributed source of information to social media. Although many 99 (58.2%) respondents had positive perceptions of the vaccine, few 16 (9.4%) had taken the vaccine. Less than quarter 24 (22.1%) planned to receive COVID-19 vaccine while majority 120 (77.9%) had no plans to, cite safety concerns. There was a statistically significant association between age (P = 0.001), level of training (P = 0.034) and uptake of COVID-19 vaccine. Conclusion and Recommendations: The level of uptake of COVID-19 vaccine was poor amongst undergraduate students in tertiary institutions in Lagos. Age and level of training of respondents were factors associated with poor uptake. It is recommended that the section of university responsible for sharing of information amongst students organises risk communication activities targeted at specific areas about COVID-19 vaccine to improve vaccine uptake amongst students.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".