Maternal health services utilisation among primigravidas in Uganda: what did the MDGs deliver?
Bibliographic record
Abstract
BACKGROUND: Achieving maternal health outcomes in the SDGs requires the implementation of more targeted policies and strategies. While the MDGs may have deepened our understanding in this regard, we know little about the trends in maternal health services utilisation among primigravidas, and how age and geographical regions could have influenced these trends. In this study, we examined utilisation of antenatal and skilled delivery services among primigravidas in Uganda, a country with one of the highest maternal mortality ratios, and where early childbearing and its attendant challenges are common. METHODS: Guided by Andersen's Behavioural Model, we fitted multivariate regression models to a pooled dataset of the 2006, 2011 and 2016 Ugandan Demographic and Health Survey (n = 3477) to understand the dynamics in Antenatal Care (ANC) and Skilled Birth Attendance (SBAs) utilisation among primigravidas. Post-estimation margins were employed to further highlight the effect of age and geographical regions. RESULTS: The analyses show an improvement in access to maternal health services among primigravidas from 2006 to 2016. Compared to 2006, primigravidas in 2016 were 48%, 24% and 2.98 times more likely to have early ANC, four or more ANC visits, and SBAs, respectively. Altogether, a primigravida in 2016 relative to 2006 was 42% more likely to meet all three maternal health service indicators. Post-estimation margins analyses on age and geographical disparities revealed that younger primigravidas have lower probability, while primigravidas in Eastern Region, one of the most deprived in the country, have the lowest probability of accessing maternal health services. Also, the study found education, wealth, women's household decision-making power, place of residence as important determinants of ANC visits and SBAs. CONCLUSIONS: Based on our findings, it is important to address the vulnerabilities of primigravidas, particularly younger individuals, in accessing early ANC. Uganda should scale-up decentralisation and integration of maternal health delivery in local communities as a strategy of addressing lingering geographical disparities, and ultimately improve maternal health outcomes in the SDGs 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".