Cultivating nutrition: exploring participants’ perspectives on nutrition gardens and nutrition education program in rural Tamil Nadu and Odisha, India
Bibliographic record
Abstract
Malnutrition persists as a critical public health concern in India, aggravated by widespread nutrition insecurity due to lack of dietary diversity. Integrating the promotion of nutrition gardens with nutrition education offers a promising strategy to mitigate these challenges, particularly among vulnerable populations. This paper examines the perceptions of households participated in an intervention to promote nutrition-sensitive agriculture and improve nutrition education to combat undernutrition in small-scale farming households in rural India. Using a mixed-methods approach, data were collected from participants in Tamil Nadu and Odisha through structured interviews, key informant interviews, and focus group discussions. The qualitative data were thematically analysed, and a SWOT analysis was conducted to assess the intervention’s strengths, weaknesses, opportunities, and threats. The findings show that by integrating nutrition-sensitive approaches into agricultural activities, the intervention has transformed traditional home gardening practices by diversifying homegrown produce. Participants highly valued the training sessions, and the provision of seeds and saplings, which facilitated the establishment of nutrition gardens and improved nutrition-related knowledge. However, while many participants reported improved nutrition knowledge, improvements in dietary diversity and overall nutrition were less commonly reported. Achieving a sustained impact will require context-sensitive implementation, sustained engagement, and addressing structural barriers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".