Dietary Practices Oflactating and Pregnant Women in Bomo Clan, Southern Ijaw Local Government Area, Bayelsa State, Nigeria
Bibliographic record
Abstract
The study investigated dietary practices in Bomo clan, Southern Ijaw Local Government Area, Bayelsa State. To set the tone for the study, cross-sectional design was adopted. Also,both probability (simple random) and non-probability (purposive) sampling techniques were used to select four (4) communities in Bomoclane. With an estimated proportion of 0.1, Cochran statistics revealed a sample size of one hundred and thirty-eight (138). However, data were retrieved from 113 (81.9) of pregnant and lactating women. Furthermore, the study adopted triangulation method for data collection. For instrument reliability, Cronbach Alpha was set at 0.7. Data collected using structured questionnaire were analyzed with simple percentage and Chi-Square. The analysis was done with the aid of Statistical Package for Social Sciences (SPSS) version 23.0. The study revealed that aroma, aroused the appetite for food intake among pregnant and lactating women, pregnant women had preference for food prepared by some one else, income level of respondents influenced dietary behaviour among others. Therefore, the study recommended thatthe Federal Ministry of Humanitarian Affairs Disaster Management and Social Development should provide an economic buffer that will assist pregnant and lactating women in meeting their dietary needs and etc.
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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 | 0.000 |
| 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) | 0.001 | 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".