Acceptance of COVID-19 Vaccination in Children among Adults attending Selected Health Facilities in Kinondoni Municipality; Dar es salaam, Tanzania: A Cross Sectional Study
Bibliographic record
Abstract
BACKGROUND: Safe and effective vaccines are crucial for controlling and containing COVID-19 pandemic. However, poor acceptance and hesitance to vaccinate limit effective utilization. In Tanzania, COVID-19 vaccines have been in use with adequate coverage in adults from 18-years old, however, the acceptability of their use in children is not well understood. This study was aimed at determining the acceptability of COVID-19 vaccination in children among adults in Dar es salaam, Tanzania. METHODS: A cross section study was conducted among adults attending outpatient clinic in Dar es salaam and were having children below 18-years at home. A self-administered questionnaire was used to collect their demographic information and their opinions regarding COVID-19 vaccine use in their children. Data was analyzed using Statistical Package for Social Sciences (SPSS version 23). Level of acceptance and other categorical variables were calculated in frequency and percentages while factors associated with COVID-19 vaccination in children were determined using binary logistic regression analysis. A type II error of less or equal to 0.05 was considered statistically significant. RESULTS: A total of 320 participants were recruited in the study. Among these, 289 (90.3%) were females. Out of all participants, 124 (38.57%) were willing for their children to receive COVID-19 vaccines upon availability and recommendation by respective authorities. Confidence in the safety of COVID-19 vaccines (Adjusted Odd Ratio= 0.03; 95% CI: 0.01-0.13; p=0.02, and perceived importance of COVID-19 vaccine use in children (AOR=0.29; 95% CI: 0.1-0.84; p=0.02) were independent factors associated with acceptance of COVID-19 vaccination in children. CONCLUSION: The level of acceptance of COVID-19 vaccination for children in this study was low (38.57%), with uncertainty around vaccine safety being the major concern. Therefore, to increase COVID-19 vaccines acceptance and uptake in children, effective public communication supported by data on safety and effectiveness of COVID-19 vaccines should be emphasized.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".