Household social capital and socioeconomic inequalities in child undernutrition in rural India: Exploring institutional and organizational ties
Bibliographic record
Abstract
barriers and support systems influencing childhood nutrition.Data collected from the discussions were analyzed for common themes in nutrition knowledge and practice.Future GHI teams will use this information to design nutrition education seminars capable of mitigating gaps in nutrition knowledge to improve nutrition practices.Using a train-the-trainer model, GHI plans to equip the CHWs with the tools to deliver these educational seminars, ensuring the sustainability of this project. Outcomes & Evaluation:The FGDs highlighted a need for further education about proper nutrition during pregnancy, exclusive breastfeeding, and complementary feeding of infants.Both child caregivers and CHWs commonly reported consuming fewer calories during pregnancy, receiving negligible antenatal care, and beginning breast milk supplementation as early as 3 weeks of age.Barriers to securing adequate nutrition included poverty, lack of breastfeeding support, lack of consistent healthcare, and a lack of general nutrition knowledge.Other factors contributing to poor nutrition included young maternal age and a community commitment to increasing caloric intake without considering nutrient density.Going Forward: Poor early childhood nutrition in rural Kenya is multifactorial.Having identified some of the contributing factors, GHI will partner with PCT to develop strategies to address the current gaps-in-knowledge.In addition to creating education seminars, GHI may also develop a nutrition manual to assist the CHWs in providing sustainable education and support to their communities.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".