‘That is because we are alone’: A relational qualitative study of socio-spatial inequities in maternal and newborn health programme coverage in rural Uttar Pradesh, India
Bibliographic record
Abstract
This qualitative study was conducted in Uttar Pradesh state, India to explore how interrelated socio-economic position and spatial characteristics of four diverse villages may have influenced equity in coverage of community-based maternal and newborn health (MNH) services. We conducted social mapping and three focus group discussions in each village, among women of lower and higher socio-economic position who recently gave birth, and with community health workers (n = 134). Data were analysed in NVivo 11.0 using thematic framework analysis. The extent of socio-economic hierarchies and spatial disparateness within the village, combined with distance to larger centers, together shaped villages’ level of socio-spatial remoteness. Disadvantaged socio-economic groups expressed being more often spatially isolated, with less access to infrastructure, resources or services, which was heightened if the village was physically distant from larger centers. In more socio-spatially remote villages, inequities in coverage of MNH services that disadvantaged lower socio-economic position groups were compounded as these groups more often experienced ASHA vacancies, as well as greater distance to and poorer perceived quality of health services nearest the village. The results inform a conceptual framework of ‘socio-spatial remoteness’ that can guide public health research and programmes to more comprehensively address health inequities within India and beyond.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".