Preferences for infant delivery site among pregnant women and new mothers in Northern Karnataka, India
Bibliographic record
Abstract
BACKGROUND: The National Rural Health Mission (NRHM) of India aims to increase the uptake of safe and institutional delivery among rural communities to improve maternal, neonatal and child health (MNCH) outcomes. Previous studies in India have found that while there have been increasing numbers of institutional deliveries there are still considerable barriers to utilization and quality of services, particularly in rural areas, that may mitigate improvements achieved by MNCH interventions. This paper aims to explore the factors influencing preference for home, public or private hospital delivery among rural pregnant and new mothers in three northern districts of Karnataka state, South India. METHODS: In-depth qualitative interviews were conducted in 2010 among 110 pregnant women, new mothers (infants born within past 3 months), their husbands and mothers-in-law. Interviews were conducted in the local language (Kannada) and then translated to English for analysis. The interviews of pregnant women and new mothers were used for analysis to ultimately develop broader themes around definitions of quality care from the perspective of service users, and the influence this had on their delivery site preferences. RESULTS: Geographical and financial access were important barriers to accessing institutional delivery services in all districts, and among those both above and below the poverty line. Access issues of greatest concern were high costs at private institutions, continuing fees at public hospitals and the inconsistent receipt of government incentives. However, views on quality of care that shaped delivery site preferences were deeply rooted in socio-cultural expectations for comfortable, respectful and safe care that must ultimately be addressed to change negative perceptions about institutional, and particularly public hospital, care at delivery. CONCLUSIONS: In the literature, quality of care beyond access has largely been overlooked in favour of support for incentives on the demand side, and more trained doctors, facilities and equipment on the supply side. Taking a comprehensive approach to quality of care in line with cultural values and community needs is imperative for improving experiences, utilization, and ultimately maternal and neonatal health outcomes at the time of delivery.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".