COVID-19 vaccine hesitancy among Nigerian youths: Case study of students in Southwestern Nigeria
Bibliographic record
Abstract
BACKGROUND AND AIM: Vaccination has been appraised to be one of the most significant public health achievements in human history. However, in countries like Nigeria, vaccine hesitancy is a public health challenge that has consistently forestalled concerted efforts made by health authorities to curb the spread of communicable diseases such as COVID-19. To improve COVID-19 vaccine acceptance via targeted interventions, it is imperative to examine the public's perception. Thus, this study aims to evaluate vaccine hesitancy among university students in Southwestern Nigeria. MATERIALS AND METHODS: The study utilized a descriptive cross-sectional design. A self-administered questionnaire was administered to a total of 366 respondents who were recruited using the convenience sampling technique and snowball approach. Data were entered and analyzed using SPSS. RESULTS: The majority of the respondents were over 18 years (88%) and were between their first and third years (81%). Over a tenth of the respondents reported having at least a loved one that had tested positive for the virus, while only 88% believed the virus is real. Furthermore, only 17% of the students had a positive attitude toward the vaccine. Although 90% of the respondents were aware of the administration of COVID-19 vaccine in Nigeria, only around a quarter were willing to take the vaccine, while 5.5% had been vaccinated. The major reasons for COVID-19 vaccine hesitancy highlighted by the respondents were concerns about vaccine side effects (21.3%), lack of trust in the authorities (26.5%), vaccine efficacy (13.1%), and diverse mystical possibilities (39.1%). CONCLUSION: The results indicate that a significant communication gap exists between the respondents and local health authorities. To enhance the acceptance of COVID-19 vaccines, extensive and targeted health promotion campaigns are required to allay specific concerns raised by the public.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".