COVID-19 vaccine hesitancy in Ethiopia: a scoping review for equitable vaccine access
Bibliographic record
Abstract
Introduction: COVID-19 vaccines are crucial for preventing severe illness from the virus. Despite their effectiveness; vaccine hesitancy, unequal access, and economic disparities hinder vaccination programs across Africa, posing significant challenges in Ethiopia. Method: This scoping review followed the methodological guidelines outlined in the Joanna Briggs Institute Reviewer's and employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Extension for Scoping Reviews (PRISMA-ScR) checklist and explanation to ensure transparency. To analyze the data, we developed tailored search strategies for key databases [HINARI, PubMed, Cochrane, African Journals Online (AJOL), and Science Direct] and gray literature sources. These strategies combined controlled vocabulary and relevant keywords. A descriptive thematic analysis was then employed to identify and categorize the various findings within the included studies. The results are presented in a narrative format, summarizing the key themes and providing a clear and comprehensive overview of the current evidence base. Results and recommendations: A review of 34 Ethiopian studies revealed significant COVID-19 vaccine hesitancy, with rates exceeding 50% in over 40% of the studies. The lowest hesitancy was found in adults from Addis Ababa (19.1%), while the highest rates were seen among healthcare workers in Oromia (69.7%) and pregnant women in Southwest Ethiopia (68.8%). Factors contributing to vaccine hesitancy in Ethiopia include being female, having only primary education, residing in rural areas, younger age, limited knowledge about the vaccine, reduced trust in authorities, and misperceptions about the risk of the virus. To address this challenge effectively, policymakers should prioritize interventions that build public trust, enhance awareness of the vaccine's benefits, and counter misinformation.
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 | 0.076 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".