Determinants of Poor Utilization and Accessibility of Immunization Services: A Qualitative Study in Selected Counties in South Sudan
Bibliographic record
Abstract
BACKGROUND: Reducing vaccine-preventable diseases mortality and morbidity in non-industrialized countries requires the enforcement of robust immunization strategies aimed at increasing coverage and reducing dropouts and missed immunization opportunities. Attaining high (>80%) immunization coverage with a low drop-out rate in South Sudan has been challenging because of the program’s high defaulting rates. This study aimed to determine the reasons for poor accessibility and utilization of immunization services in counties earmarked for Fragility, Emergency and Refugees (FER) in South Sudan. METHODS: A descriptive phenomenological study design was conducted across four counties of Northern Bahr El Ghazal, South Sudan, between May 2019 and December 2020 in which 42 focused group discussions and key-informant interviews involving the community and primary healthcare centers and units were conducted. Relevant EPI (Expanded program on immunization) tools were reviewed and data were analyzed using thematic analysis. RESULTS: The main reasons identified were negative attitudes towards healthcare workers and immunization services, competing priorities of the caregivers, delayed opening of the immunization sessions, insufficient cold chain facilities, inadequate knowledge and information about immunization services, and non-availability of vaccines at the health facility. CONCLUSIONS: A plan to supply adequate vaccines and related supplies to the counties by identifying stock levels in time must be a priority. Health facility micro-plan development and implementation should be supported by increased funding for the implementation of outreach and mobile sessions to reach missed children, intensified door-to-door health awareness, and regular community meetings to increase vaccine uptake.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".