Parental Knowledge of Malnutrition as a Cause of Infant and Child Mortality Rate in Torbu Community, Sierra Leone
Bibliographic record
Abstract
Malnutrition is a significant public health problem over the world, with severe impact in developing countries, including Asia and Africa. This paper present to assess malnutrition as a cause of infant and child mortality rate in Torbu community in Bo city, Sierra Leone. We used a community-based cross-sectional survey. A total of 80 mothers with children of under five years were selected using convenience sampling from the different divisions of Bo city. The findings showed that 48 (60%) of the respondents have never been sensitized nor have any knowledge about Malnutrition, of which 16 (50%) were informed via radio while 50 (63%) were not aware of any nutrition facility around the community. Besides, 56 (70%) of the respondents did not practice exclusive breastfeeding, 64 (80%) fed their children on only carbohydrates mainly in the form of rice, and 26 (32.5%) had one meal per day. 64 (80%) reported their children to have been admitted due to malnutrition (with mainly protein-energy malnutrition), while over a half 46 (57%) reported having lost a child to malnutrition. Thus, we recommended more nutrition education to address the poor Infant and Young Child Feeding (IYCF) practices as well as targeted health interventions to mitigate the devastating effects of child malnutrition in the district.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".