UTILIZATION OF MATERNAL HEALTH SERVICES AMONG INTERNAL MIGRANTS IN MUMBAI, INDIA
Bibliographic record
Abstract
This study aimed to understand access to maternal health care and the factors shaping it amongst poor migrants in Mumbai, India. A cross-sectional mixed methods approach was used. It included multistage cluster sampling and face-to-face interviews, through structured interview schedules, of 234 migrant women who had delivered in the two years previous to the date they were interviewed. Qualitative in-depth interviews of migrant women, health care providers and health officials were also conducted to understand community and provider perspectives. The results showed that access to antenatal care was poor among migrants with less than a third of them receiving basic antenatal care and a quarter delivering at home. Multivariate analysis highlighted that amongst migrant women those who stayed in Mumbai during pregnancy and delivery had better access to maternal health care than those who went back to their home towns. Poor maternal health care was also due to weaker demand for health care as a result of the lack of felt-need among migrants due to socio-cultural factors and lack of social support for, and knowledge of, health facilities in the city. Supply-side factors such as inadequate health infrastructure at primary and secondary levels, lack of specific strategies to improve access to health care for migrants and cumbersome administrative procedures that exclude migrants from certain government programmes all need to be addressed. Migrants should be integral to the urban development process and policies should aim at preventing their exclusion from basic amenities and their entitlements as citizens.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".