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Record W4412529289 · doi:10.5206/ijoh.2023.3.18960

Experiences of Maternal and Child Health Care Seeking Among Homeless Women in India: Findings from a Qualitative Study

2025· article· en· W4412529289 on OpenAlexvenueno aff
Bincy Mathew, Devaki Nambiar

Bibliographic record

VenueInternational Journal on Homelessness · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchPsychologyMaternal healthDevelopmental psychologyMedicineClinical psychologyEnvironmental healthSociologyHealth servicesPopulationSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.425
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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