Covid-19 Vaccine Acceptance and Predictors of Hesitance among Antenatal care Booked Pregnant in North West Ethiopia 2021: Implications for Intervention and Cues to Action
Bibliographic record
Abstract
Despite efforts to decrease the burden, vaccine hesitancy is increasing worldwide and deterring efforts to control the spread of COVID-19 after the approval of SARS-CoV-2 vaccines. This study aims to assess levels of COVID-19 vaccine acceptance and predictors of hesitancy for pregnant women attending antenatal care in Ethiopia.
 Methods: Facility-based cross-sectional study was employed among 336 pregnant women from April 7 to June 10, 2021. The systematic random sampling technique was used to select pregnant from three health centers. Epi-Data version 3.2 and STATA/14 software were used for both data entry and analysis, respectively. A Logistic regression model was used to identify predictors of COVID-19 vaccine hesitance. Adjusted odds ratio (AOR) with a 95% confidence interval was used to estimate the strength of association at P<0.05.
 Result: This study included 336 pregnant mothers who were booked ANC in three health centers. The overall levels of COVID-19 vaccine acceptance among pregnant mothers were 79.17 %(95%CI: 74.5 --83.2). Whereas, having poor attitude towards COVID-19 vaccines (AOR=9.4; 95%CI: 3.7--21.1, P<0.001), monthly income ≤118.5 US dollar (AOR =6.3; 95%CI: 2.9--12.2, P<0.002), Mother who are illiterate and started ANC (AOR=9.5; 95%CI: 4.6--22.6, P<0.001), Being unplanned pregnant (AOR =7.5; 95%CI: 3.6-11.2, P<0.002), first time ANC initiated (AOR =4.2; 95%CI: 2.9--15.1, P<0.001), and pregnant didn’t used social media (AOR= 6.0: 95%CI: 2.5--14.6, P< 0.02) were significantly associated with COVID-19 Vaccine hesitance.
 Conclusion: The acceptability of the COVID-19 vaccine among pregnant mothers was insufficient compared with previous research. Health care workers should provide health education during ANC visits to change their negative attitude and reassurance for the safety and effectiveness of the COVID-19 vaccine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".