Association of identification of facility and transportation for childbirth with institutional delivery in high priority districts of Uttar Pradesh, India
Bibliographic record
Abstract
BACKGROUND: Timely and skilled care is key to reducing maternal and neonatal mortality. Birth preparedness involves preparation for safe childbirth during the antenatal period to reach the appropriate health facility for ensuring safe delivery. Hence, understanding the factors associated with birth preparedness and its significance for safe delivery is essential. This paper aims to assess the levels of birth preparedness, its determinants and association with institutional deliveries in High Priority Districts of Uttar Pradesh, India. METHODS: A community-based cross-sectional survey was conducted between June-October 2018 in the rural areas of 25 high priority districts of Uttar Pradesh, India. Simple random sampling was used to select 40 blocks among 294 blocks in 25 districts and 2646 primary sampling units within the selected blocks. The survey interviewed 9458 women who had a delivery 2 months prior to the survey. Descriptive statistics were included to characterize the study population. Multivariable logistic regression analyses were performed to identify the determinants of birth preparedness and to examine the association of birth preparedness with institutional delivery. RESULTS: Among the 9458 respondents, 61.8% had birth preparedness (both facility and transportation identified) and 79.1% delivered in a health facility. Women in other caste category (aOR = 1.24, CI 1.06-1.45) and those with 10 or more years of education (aOR = 1.68, CI 1.46-1.92) were more likely to have birth preparedness. Antenatal care (ANC) service uptake related factors like early registration for ANC (aOR = 1.14, CI 1.04-1.25) and three or more front line worker contacts (aOR = 1.61, CI 1.46-1.79) were also found to be significantly associated with birth preparedness. The adjusted multivariate model showed that those who identified both facility and transport were seven times more likely to undergo delivery in a health facility (aOR = 7.00, CI 6.07-8.08). CONCLUSION: The results indicate the need for focussing on marginalized groups for improving birth preparedness. Increasing ANC registration in the first trimester of pregnancy, improving frontline worker contact, and optimum utilization of antenatal care check-ups for effective counselling on birth preparedness along with system level improvements could improve birth preparedness and consequently institutional delivery rates in Uttar Pradesh, India.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".