Prevalence of maternal near miss and community-based risk factors in Central Uganda
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of maternal near-miss (MNM) and its associated risk factors in a community setting in Central Uganda. METHODS: A cross-sectional research design employing multi-stage sampling collected data from women aged 15-49 years in Rakai, Uganda, who had been pregnant in the 3years preceding the survey, conducted between August 10 and December 31, 2013. Additionally, in-depth interviews were conducted. WHO-based disease and management criteria were used to identify MNM. Binary logistic regression was used to predict MNM risk factors. Content analysis was performed for qualitative data. RESULTS: Survey data were collected from 1557 women and 40 in-depth interviews were conducted. The MNM prevalence was 287.7 per 1000 pregnancies; the majority of MNMs resulted from hemorrhage. Unwanted pregnancies, a history of MNM, primipara, pregnancy danger signs, Banyakore ethnicity, and a partner who had completed primary education only were associated with increased odds of MNM (all P<0.05). CONCLUSIONS: MNM morbidity is a significant burden in Central Uganda. The present study demonstrated higher MNM rates compared with studies employing organ-failure MNM-diagnostic criteria. These findings illustrate the need to look beyond mortality statistics when assessing maternal health outcomes. Concerted efforts to increase supervised deliveries, access to emergency obstetric care, and access to contraceptives are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".