Exploring Maternal and Child Health Among Tribal Communities in India: A Life Course Perspective
Bibliographic record
Abstract
India experiences high rates of maternal and infant mortality and morbidity, with tribal communities disproportionately affected. Tribal populations frequently live in unfavorable socio-economic conditions and deficient social health indicators, culminating in adverse health consequences. Using a life course perspective, this qualitative study explored risks over the life course that contribute to maternal and child health problems among tribal populations in India. Additionally, the study examined barriers to utilization of healthcare services during the pregnancy and postpartum periods. Data collection occurred between 2017 and 2019 through participant observation, key informant interviews (n = 7) and in-depth interviews (n = 68) and a focus group (n = 7) with tribal women from the Madia-Gond tribe in the Indian state of Maharashtra. Additionally, verbal autopsies were conducted with relatives of three deceased women and five infants from the tribe. Multiple risk factors operating at different socio-ecological levels and developmental stages of life were associated with maternal and child health problems among the tribe. These included adherence to traditional harmful practices, limited access to nutritional diet, women's health neglected due to the double burden of domestic and professional labor, and a lack of accessible and well-equipped medical facilities. Inaccesibility stemmed from factors including extreme poverty, geographical isolation, and suboptimal healthcare infrastructure. There is need for provisions to promote access to care and to promote education and awareness centered on evidence-supported healthcare, particularly targeted towards expectant mothers. The implementation of nutritional support programs may help mitigate high maternal and child mortality and morbidity rates prevalent among tribal populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".