A systematic approach to food variety classification as a tool in dietary assessment: A case study of Kitui district
Notice bibliographique
Résumé
Maternal and child mortalities in sub-Sahara Africa can be alleviated through improvement in food and nutrition security. Part of this strategy includes complementing supplementation, fortification and public health improvement efforts by diversifying dietary habits through identification and utilization of various types of local food sources. In Kenya, inadequate evidence-based information on nutrient variations within species still limits the adoption of dietary diversity policies, particularly in support of the implementation of food and nutrition programmes. The gap between knowledge and practice, therefore, needs to be addressed. Dietary diversity is commonly tabulated using computed scores for food diversity (count of food groups consumed during the recall period) and food variety (count of all dietary items consumed during the recall period up to the species level). This simplification of dietary diversity scores is attributed to the complexity involved in collecting accurate information on varieties under each species consumed. This has led to an urgent need to develop simple, consistent, effective and variety-level sensitive methods of measuring food biodiversity within peoples’ diets. This paper presents a pilot study carried out with an aim of demonstrating the steps involved in applying a food biodiversity sensitive indicator in food consumption studies using a variety-level biodiversity tool in Kitui district, Kenya. A community food list with variety names and photos was developed and was used during household dietary assessment. The target subjects were women and children (under five years). The indicator was tested among women and children under the age of five and, for comparison, a food diversity score was also administered as an indicator of dietary diversity. Results showed that the food variety scores were more indicative of the food biodiversity resources consumed in the community than food diversity scores. The mean variety scores for mothers in the last 24 hours, 7 days and 1 month preceding the survey were 12.80(±4.11), 21.06(±6.37) and 24.43(±7.44) respectively while those for children were 12.93(±4.47), 20.80(±6.98) and 23.88(±8.13) respectively. The mean food diversity scores for mothers in the last 24 hours, 7 days and 1 month preceding the survey were 7.49(±1.25), 8.60(±0.73) and 8.73(±0.64), respectively while those for index children were 7.36(±1.39), 8.42(±1.01) and 8.55(±0.95), respectively. The differences in mean values for both variety and diversity scores for one day, one week and one month were statistically significant among women and children (p<0.001). This approach could provide an alternative indicator for computing dietary diversity in future.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».