Self-care practices of pregnant women: A qualitative study in a Nigerian rural community
Bibliographic record
Abstract
Context: Inadequate self-care during pregnancy is a contributor to the poor maternal health indices in Cross River State; including the high maternal mortality ratio of 2,000/100,000 live births. Objectives: The objectives of the study were to identify self-care practices adopted by women during pregnancy and delivery and to identify barriers to quality self care. Methods: Focus Group Discussions, Key informant interviews and in-depth interviews were conducted among pregnant women, women of reproductive age and other stakeholders in Biase Local Government Area of Cross River State. Results: The study revealed inadequate knowledge and practice of self-care during pregnancy. Some pregnant women were unable to recognize early signs of pregnancy while others, especially young unmarried girls, tried to hide the pregnancy. Barriers to effective self-care identified included myths and misconceptions, especially the belief that health-related events during pregnancy are caused by witches and wizards, lack of preparation for pregnancy and abandonment of pregnant women by partners, usually due to unwillingness and/or inability to father the child. Conclusion: There is need for pre-marital and pre-natal counselling and health education so as to address identified gaps in knowledge and practice and lack of male involvement in maternal healthcare.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".