Factors Contributing to the Late Commencement of Antenatal Care at a Rural District Hospital in Lesotho
Bibliographic record
Abstract
Antenatal care (ANC) is a key approach aimed at improving maternal and infant health. Numerous factors are associated with late commencement of antenatal care. Sub-Saharan Africa countries are exception to the problem of late commencement of antenatal care. The qualitative, explorative, descriptive and contextual approach was followed. The pregnant women meeting the inclusion criteria, were above 16 weeks and attended antenatal care at the time of the study. Different authorities granted permission to conduct face-to-face, unstructured in-depth interviews. Tesch approach enabled the qualitative researchers to immerse themselves through systematic organization and synthesis of data to create manageable units. an independent co-coder also analyzed data independently. Afterwards, they met and agreed on specific themes and sub-categories. The following five themes emerged; personal and family factors, cultural beliefs and practices, health systems and poor infrastructure. Measures aimed at improving accessibility to the health centers include; road infrastructure, telecommunication and more client centered services. Improvement of early commencement of antenatal services becomes an ideal approach influencing excellent maternal and neonatal outcomes. Therefore, government initiatives aimed at empowering communities on the benefits of commencing antenatal care on time is necessary.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".