“Does a healthy man need vaccination?”: Attitudes of older adults toward COVID-19 vaccine in South-East Nigeria
Bibliographic record
Abstract
The COVID-19 pandemic appears to be impeding the progress of the United Nations’ Sustainable Development Goal and the African Union’s Agenda 2063 in achieving optimal health and well-being for individuals, particularly older adults. Numerous older adults have succumbed to the virus, exacerbating existing global health challenges. In response, scientists worldwide have developed a vaccine to alleviate the substantial disease burden. The Nigerian government has mandated the prioritized vaccination of older adults. This study aims to investigate the attitudes of older adults toward the COVID-19 vaccine. Data were collected from 32 older adults through in-depth interviews and focus group discussions. Thematic analysis was employed to derive meaningful patterns from the collected data. The findings reveal a prevailing lack of awareness among older adults regarding the COVID-19 vaccine. They asserted that they perceived no need for vaccinations, asserting their current state of health. In addition, concerns were raised about potential adverse effects of the vaccine, including the onset of other illnesses. This study suggests that the Nigerian government, through its orientation agencies, undertakes comprehensive public education campaigns highlighting the importance of COVID-19 vaccine uptake.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".