Uptake, adverse effect, and associated factors of COVID-19 vaccine among those living with human immunodeficiency virus, at Bole sub-city health facility Addis Ababa, Ethiopia
Bibliographic record
Abstract
COVID-19 vaccination in African people living with human immunodeficiency virus remains understudied, with limited research in Ethiopia that fails to consider contextual differences. To assess uptake, adverse effects, and associated factors of COVID-19 vaccine among PLWHA in Addis Ababa, Ethiopia, 2022. An institutional cross-sectional study design was employed among 404 participants. Sample selected by systematic random sampling technique. Descriptive and inferential statistical analyses were carried out. Finally, results were presented using Crude Odd Ratio, Adjusted Odd Ratio, and 95% Confidence Interval. Result: Out of all participants, 79% (314) received at least one dose of any type of COVID-19 vaccine, with varying percentages taking one (29.3%), two (50.3%), and three (20.4%) doses of the vaccine. Being knowledgeable (moderate and good) (AOR = 0.06, 95% CI: 0.01, 0.5) and medium attitude (AOR = 1.1, 95% CI: 0.7, 1.3) had a statistically significant association with the uptake of the COVID-19 vaccine. The prevalence of adverse events was 27.8% (110). More than three-quarters of participants were vaccinated for the COVID-19 vaccine. Moderate knowledge and medium attitude have a significant association with the uptake of the COVID-19 vaccine. Nearly a quarter of participants experienced adverse events related to COVID-19. Continued efforts are essential to overcome barriers to achieving full vaccination coverage for the most vulnerable in low-income countries. Addressing hesitancy, monitoring side effects, and implementing effective communication and strategies are crucial for widespread COVID-19 vaccination and public health safety in these regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".