Evaluation of the Nutrition Surveillance System, Sana’a City, Yemen, 2021: Cross-sectional Study
Bibliographic record
Abstract
Background Malnutrition remains one of the most common causes of morbidity and mortality among children in low- and middle-income countries. It is one of the important problems that showed an increasing incidence in Yemen. The Nutrition Surveillance System started in 2018 as a pilot in five governorates to ensure that difficulties of public health importance are monitored efficiently. Objective This study aims to assess its usefulness and the performance of the system attributes, and to identify strengths and weaknesses to make recommendations for improvement. Methods The Centers for Disease Control and Prevention’s updated guidelines for the evaluation of public health surveillance were used to evaluate the Nutrition Surveillance System in Sana’a City. Qualitative and quantitative attributes were measured through desk review and in-depth interviews with stakeholders from different levels by using a semistructured questionnaire for collected data. The percent mean of total scores was used for the final rank of the performance as very poor (<40%), poor (40%<60%), average (60%<80%), good (80%<90%), and excellent (≥90%). Epi Info version 7.2 was used for data entry and analysis. Results The Nutrition Surveillance System was found to be useful and flexible, with overall scores of 100% and 80%, respectively, and the overall system performance was average (76%). The highest attribute score was 83% for simplicity, and the lowest score was 67% for stability. Simplicity and acceptability at the governorate and district levels were good, but at the health facilities level, they were average. Timeliness of report and completeness of forms and data were 100% and 95%, respectively. The main strength of the Nutrition Surveillance System was continuous expansion in opening new health facilities and that the quality of data was strong with updated databases. Conclusions The Nutrition Surveillance System in Sana’a City was found to be useful and met its main objective. Overall levels of system performance were average. Regular training for health staff at the health facilities and gradual replacement of donors with government funds are recommended.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".