Exploring reasons for poor dietary diversity in Karnataka, India - a mixed-methods study
Bibliographic record
Abstract
Abstract Background More than a third of the world's malnourished population resides in India, and micronutrient deficiency is a common cause of malnutrition in India. Over 80% of India's population suffer from this condition, especially due to inadequate intakes of riboflavin, folate, vitamins B6 and B12. In this study, we aimed to gain a better understanding of the reasons for poor dietary diversity in Karnataka, India. Methods From March to October 2024, 28 community health workers were trained and deployed across rural and peri-urban (around Bangalore) communities in Karnataka. During the initial engagement, information on resident demographics and dietary patterns over the past 24 hours were collected using a proprietary eHealth platform, then residents were educated on the importance of a balanced diet and eating diverse food groups. Selected residents were interviewed 2-4months later to assess dietary changes and their motivations. Study was done in collaboration with Bayer. Results 47,423 residents were engaged in Karnataka (43% in peri-urban areas). In total, 87% had poor dietary diversity - although this was poorer in peri-urban (99.6%) compared to rural areas (78%). This was attributed to the availability of fresh, local foods in rural areas, and the easier accessibility of processed foods in peri-urban areas. 959 residents were interviewed post-engagement, and found that only 6% and 1% of residents with poor dietary diversity showed improvements in their dietary patterns post-engagement in rural and peri-urban areas respectively. This was explained by the mismatch between actual and perceived dietary adequacy - suggesting a significant knowledge gap amongst residents. Dietary changes were also hindered by economic constraints, availability, and dietary habits Conclusions Addressing micronutrient deficiency in Karnataka requires a multi-pronged approach that targets individual knowledge, behaviour change, and systemic structures that hinder improvements. Key messages • Effective nutrition education programmes need to be explored in Karnataka. • Systemic solutions that address farmers and food retailers are needed to improve micronutrient deficiency in Karnataka.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".