Evaluation of Cold Chain Equipment Platform Solar Fridges Project in Upper Nile State Republic of South Sudan
Bibliographic record
Abstract
BACKGROUND: A cold chain is a temperature-controlled supply chain with uninterrupted chain of activities that maintain a given temperature range that keep and maintain the quality of vaccines. Vaccines move through complex procedures and processes that require special attention and care. Effective cold chain systems require efficient end-to-end vaccine storage, handling, and stock management to maintain vaccines under strict temperature control of between 2 °C and 8 °C (for almost all vaccines). METHODS: A descriptive cross-sectional study design and mixed (qualitative and quantitative) research approach is employed to conducting the research. Data were collected through face to face in-depth interviews and questionnaires from Vaccinators and key persons from IPs. Prior to data collection, ethical approval was obtained from national Ministry of Health, Directorate of planning and M&E, and the University of Rwanda, Research Committee Board. Accordingly, data were collected after seeking the personal consent sought from the participants. RESULTS: The key findings from this study showed that the cold chain coverage has been improved compared to the result obtained in EPI coverage survey conducted in 2017. The results revealed that inadequate availability of EPI cold technicians, lack of fridge spare parts, trained staff, and adequate vaccine forecasting was the major challenges at county and the health facility level. CONCLUSIONS: The main factor that contributed positively in strengthening vaccines supply chain system in the Upper Nile State was the substantive increase in cold chain coverage. However, the role of the CCEOP in improving immunization coverage is still doubted due to lack of regular preventive maintenance, spare parts, and EPI technicians. The major challenges that affect the vaccines relevance, effectiveness, efficiency and sustainability were found to be mainly poor vaccines handling, due to inadequate knowledge on cold chain management.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".