Maternal Employment, Child’s Caring Practices and Nutritional Status in Northern Ghana
Bibliographic record
Abstract
Mothers as caregivers exert strong influence over child feeding and caring practices. Maternal employment may influence child caring practices thus affecting the child’s nutritional status. The purpose of this study was to examine the effect of maternal employment status, on child caring practices and the nutritional status of children under-5 in Savelugu, Northern Ghana. This was a cross-sectional survey involving 400 mothers and their children under-5 years old. Data collection took place between February and May, 2013 through a house-to-house visit using a structured questionnaire designed for the study. Information collected included employment status, occupation type and mothers working hours away from home, feeding and caring practices and anthropometric measurements of their children. About 85.8% of respondents were employed. Together farmers and traders made approximately 76% of the respondents. Approximately 55% of Mothers had at least primary education. Around 85.1% of the employed mothers look after their children whiles carrying out their daily work. Employed mothers spent between 5 to 6 hours/day away from home without their children but unemployed mothers were mostly with their children. Occupation status has a significant effect on child caring practices with those unemployed being better (P<0.05). About 72.0% and 70.3% respectively of unemployed and employed mothers indicated they introduced complementary feeding at 6months. Child caring practices were better among unemployed mothers compared to employed mothers among the study population. Stunting and wasting rates were high among children of both employed and unemployed mothers.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".