Exploring reasons for poor dietary diversity in Karnataka, India - a mixed-methods study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».