Infant Feeding Practices, Prevalence of Adolescent Motherhood and Malnutrition among Infants in Mangu Local Government Area, Plateau State, Nigeria
Bibliographic record
Abstract
Background: Adolescent motherhood and malnutrition among children are significant challenges in Africa, but there is limited data on the impact of adolescent motherhood on their children’s health and nutrition. This study assessed infant feeding practices, prevalence of adolescent motherhood, and malnutrition among infants in Mangu local government area (LGA). Methodology: A cross-sectional survey using multistage sampling was conducted. Validated questionnaires were used to collect socio-demographic data, and appropriate tools were used for anthropometric measurements. Data were compared with established standards. Descriptive statistical tools, chi square, Pearson correlation, and independent sample t-test were used for data analysis, with significance set at p < 0.05. Results: A total of 200 mothers completed the study. The majority of the infants (78.5%) were less than 6 months old, and 21.5% were 6–12 months old. Breastfeeding initiation within 1 hour was reported by 39% of mothers, while 38% practiced prelacteal feeding. Only 28.5% practiced exclusive breastfeeding, and all mothers breastfed their babies. The prevalence of adolescent motherhood was 37.5%. The prevalence of stunting, wasting, and underweight among infants were 29.5%, 12%, and 8.5%, respectively. Children of adolescent mothers had higher rates of severe stunting compared to children of mothers above 19 years of age. There were significant differences (p = 0.017 and p = 0.029) in stunting rates and weight-for-age indices between children of adolescent mothers and mothers above 19 years of age. Conclusion: Adolescent motherhood contributes to chronic malnutrition in children, and there is a high prevalence of malnutrition among infants in Mangu LGA, Plateau State.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".