Experiences of Maternal and Child Health Care Seeking Among Homeless Women in India: Findings from a Qualitative Study
Bibliographic record
Abstract
Rapid urbanization affects the maternal and child health of the urban poor, including the homeless, who may be excluded from universal maternal and child health programs. We carried out in-depth interviews with five homeless pregnant and lactating women alongside participant observation in two locations in Delhi, India. Data were analysed in ATLAS.ti using Blas and Kurup’s Priority Public Health Conditions Framework. Precarious housing situations, irregular work and income (aspects of context and position), interacted with harmful physical exposures, inadequate drinking water and sanitation facilities (in exposure) alongside vulnerabilities pertaining to gender-based disadvantage and various difficulties acquiring and maintaining formal identity papers (vulnerabilities). We found that women had chronic, unresolved health problems; were unable to access antenatal and intranatal services, and when they did, some reported disrespect and violations, with variations in experience that strongly affected the proposed and future course of health-seeking. If basic and longstanding programmes like those related to maternal and child health (MCH) are to be truly universal (in the way they claim to be), and indeed if the right of all women to health is to be respected, protected and fulfilled, this is an important population whose access and barriers, experiences and vulnerabilities must be understood and intervened upon.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".