Profiling Maternal and Child Health Care in the Tribal Communities of Telangana State: An Anthropological Enquiry
Bibliographic record
Abstract
Sexual and reproductive health aspects of tribal communities have been a matter of concern for various scholars. The present study, conducted among the tribals in one of the Integrated Tribal Development Agency areas (Mannanoor ITDA) in the state of Telangana, is primarily set to enrich the existing knowledge concerning reproductive health. The article particularly focuses on the antenatal/prenatal care, intranatal care, and reproductive outcomes among the tribal communities. The article attempts to look into the age of these women at marriage and various conceptions, the spacing between conceptions, and the outcome of the conceptions. Besides this, efforts have been made to gather data on the place of delivery, antenatal care, and birth weights of infants. The study could identify that about a quarter were low-birth-weight babies. The study also showed that overall reproductive wastage is quite significant in the tribal communities in the study area, even though the women in the area were found to be avoiding marriages at a very young age. By and large, the study reveals that maternal and child health practices are relatively satisfactory in the tribal communities in the study area.
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 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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".