Assessment of Home-Based Care for Young Child (HBYC) Program in Aspirational Districts of Madhya Pradesh, India: A Cross-Sectional Study
Bibliographic record
Abstract
Background and Objectives: In 2018, the Government of India launched the Home-Based Care for Young Child (HBYC) programme, which includes five scheduled home visits per quarter for children aged 3 to 15 months to enhance early childhood development. Evaluate the understanding and behaviors of Accredited Social Health Activist (ASHAs), other health workers, and mothers about HBYC. Cross-sectional assessment design with ASHAs, AWWs, ANMs, ASHA, and mothers of children aged 3 to 15 months as participants. Material and Methods: An evaluation was conducted on the knowledge and practices of 801 ASHAs, 200 other health functionaries, and 787 mothers regarding exclusive breastfeeding, complementary feeding, hand washing, iron folic acid (IFA) and oral rehydration solution (ORS) supplementation, and danger referral signs in eight aspirational districts of Madhya Pradesh. Results: 88% of ASHAs demonstrated accurate understanding of ORS, 85% of supplemental feeding, 85% of the adequacy of IFA, and 47% of danger indicators for child referral. 85% of moms were aware of exclusive breastfeeding, 40% knew about supplementary feeding, and just 18% knew the precise preparation of ORS. A statistically significant relationship was found between ASHAs doing home visits and the presence of ORS in households, as well as mothers’ understanding of the proper initiation of IFA (p < 0.001). Conclusion: The survey indicated that most health functionaries were knowledgeable of the duties, responsibilities, and critical activities associated with HBYC. There was a lack of information transmission by health workers, resulting in insufficient implementation of behaviors among mothers regarding HBYC. This requires implementing relevant measures ranging from enhancing the health system to creating capacity in order to speed up the adoption of the HBYC programme.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".