Bibliographic record
Abstract
BACKGROUND: Over 99% of mothers who die of complications related to pregnancy and childbirth each year are found in developing countries.3 In Uganda, maternal mortality is estimated at 435 deaths per 100,000 live births. We sought to understand the factors influencing frequency and timing of antenatal care in Uganda in order to inform policy on the key aspects that need to be influenced. METHOD: We used data from the Uganda Demographic and Health Survey (UDHS) 2006 and employed both descriptive and quantitative approaches (probit estimation). After a probit estimation, we generated marginal effects to interpret the results as probabilities of utilisation of antenatal care given particular background characteristics. RESULTS: On average, only 17% and 47% of mothers initiate the first antenatal visit in the first trimester and attain at least four antenatal visits, respectively. The timing and frequency of antenatal visits were significantly associated with education of the mother and her partner, wealth status, regional disparities, religious differences, access to media, maternal autonomy in taking a health decision, occupations of the mother and her partner, timing of pregnancy, birth histories, and birth order. CONCLUSION: Efforts are needed to educate girls beyond secondary level, establishment village outreach clinics with qualified staff to attract the hard-to-reach women, and to ensure universal access to prenatal care services irrespective of the ability to pay. Media penetration should also be increased amongst the population and this channel can be used to disseminate a standard piece of information concerning what pregnant women should expect and do during the prenatal period.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".