Factors Influencing Childhood Immunization Coverage in Juba County, South Sudan: A Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Introduction: The study aimed to investigate the immunization status of children aged 1 to 2 years in Juba County, South Sudan, and to identify factors associated with their immunization coverage. Methods: A cross-sectional survey design was employed, with Juba being purposively sampled and the Payams in Juba being stratified, followed by the random sampling of villages within these Payams. Data was collected through structured interviews and analyzed using SPSS v22, incorporating descriptive and inferential statistics. Findings: The study revealed that more than three-quarters of the children had received all the recommended vaccines, with the BCG vaccine being the most commonly received, while the measles vaccine was the least received. The study identified several factors associated with immunization coverage, including parental age, religious affiliation, knowledge of the benefits of immunization, maternal economic activity, and perceived flexibility of immunization services provided at healthcare facilities. Notably, the prevalence of reception of all vaccines was lower among children whose parents were aged 18–25 years and born-again Christians, while it was higher among children whose parents recognized the benefits of immunization. Additionally, children whose mothers were engaged in economic activities were less likely to receive all primary vaccines. Furthermore, the study found that the flexibility of immunization services at healthcare facilities was associated with lower immunization coverage. Conclusion: The study highlighted that while the proportion of children in Juba receiving all vaccines is relatively high, it still falls below the global standard of 90%. The immunization status of children in Juba was found to be primarily influenced by parental characteristics, with institutional characteristics playing a smaller role. These findings underscore the importance of targeted interventions to address specific parental and institutional factors that may hinder optimal immunization coverage in Juba County, South Sudan.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".