Does the Quality of Antenatal Care Predict Health Facility Delivery Among Women in Kenya? Further Analysis of KDHS Data 2008/09
Bibliographic record
Abstract
Improving maternal health remains a priority in Kenya and beyond. It is essential that women get good medical care before, during and after pregnancy to reduce maternal mortality. Skilled delivery care remains low in Kenya and maternal mortality rate high regardless of numerous ongoing interventions. Antenatal care is known to promote maternal and fetal well-being. However less than 50% of women make the recommended four or more antenatal care visits, missing out on key services such as urine and blood tests, and advice on possible pregnancy complications, that determine the quality of ANC. This study examines how the number of ANC visits and the quality of those visits predict health facility use at delivery. Maternal health data from DHS of 2008/2008 in Kenya was analyzed using Stata 11.0 software. Logistic regression was used to evaluate relationships between facility delivery and predictor variables in univariate and multivariate models. Estimates were based on 95% confidence inteval. The models were examined at 95% CI and 80% power and adjusted for maternal age at last birth, education, place and type of residence, level of exposure to media, mother’s religion, wealth index and birth order. The quality of ANC was an index developed based on the number of services received during ANC visits.The quality of ANC visits progressively increased the likelyhood of health facility delivery. Supply and demand should be intervention targets to ensure that women know and understand the services to demand. Health facilities should also be sufficiently prepared and ready with the services.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".