Prevalence and Determinants of Diarrhoea Disease amongst Under-Five Children Attending Immunization Clinic in a Secondary Health Facility in Benin City
Bibliographic record
Abstract
Introduction: Diarrhoea is a major public health challenge in low and middle-income countries. It is the second leading cause of mortality in children under the age of five. It is also recognized that exposure to diarrhoea pathogens in developing countries is associated with such factors as quality and quantity of water, availability of toilet facilities, housing conditions, level of education, household economic status, place of residence, feeding practices, and the general sanitary conditions (personal or domestic hygiene) around the house. These factors are still prevalent in many Nigerian communities. This study therefore sought to determine the prevalence of diarrhoea and risk factors among under-five children seen at immunisation clinic of a secondary hospital in Nigeria. Methodology: This was a cross-sectional study of 210 mothers attending well-baby clinic of a multi-specialist secondary health facility in Benin-City, South-South, Nigeria. Information relating to socio-demographics and occurrence of diarrhoea in their wards was obtained using ore-tested questionnaire. Data obtained were tabulated and analyzed using IBM SPSS version 21. Chi-square was used to determine the relationship between qualitative variables the statistical significance was set at P<0.05. Result: Prevalence of diarrhoea was 38.6% with a mean number of episodes per year of 2.14. Prevalence of dysentery was 4.3%. There was significant association between age, nutritional status, source of water and the occurrence of diarrhoea (P values of 0.04, 0.038 & 0.02 respectively). Conclusion: This study shows a high prevalence of diarrhoea amongst under-5 children in our environment.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".