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Enregistrement W4377013465 · doi:10.1088/2752-5309/acd6b0

Assessing dietary adequacy and temporal variability in the context of Covid-19 among Indigenous and rural communities in Kanungu District, Uganda: a mixed-methods study

2023· article· en· W4377013465 sur OpenAlexafffund
Giulia Scarpa, Lea Berrang‐Ford, Sabastian Twesigomwe, Paul Kakwangire, Maria Galazoula, Carol Zavaleta-Cortijo, Kaitlin Patterson, Didacus B. Namanya, Shuaib Lwasa, E. Ninshaba, Mary Kiconco, Janet Cade

Notice bibliographique

RevueEnvironmental Research Health · 2023
Typearticle
Langueen
DomaineNursing
ThématiqueChild Nutrition and Water Access
Établissements canadiensUniversity of Guelph
Organismes subventionnairesCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchDepartment of Health and Social CareInternational Development Research Centre
Mots-clésContext (archaeology)IndigenousEnvironmental healthGeographyAnthropometryMedicineDietary diversityDemographyGerontologySocioeconomicsFood securityBiologyEcologyAgriculture

Résumé

récupéré en direct d'OpenAlex

Abstract Dietary adequacy is hard to achieve for many people living in low-income countries, who suffer from nutritional deficiencies. Climate change, which alters weather conditions, has combined with other cascading and compound events to disrupt Indigenous communities’ food systems, limiting the consumption of adequate diets. The aim of this work was to conduct a proof-of-concept study exploring dietary adequacy, and to investigate evidence for temporal variation in the dietary intake of Indigenous and non-Indigenous communities in Kanungu District, Uganda in the context of the Covid-19 outbreak. We randomly selected 60 participants (20 mothers, 20 fathers and 20 children aged between 6 and 23 months) from two Indigenous Batwa and two Bakiga settlements. A mixed-methods study with concurrent qualitative and quantitative data collection was conducted. Monthly dietary intake data were collected from each participant from February to July 2021 through 24 h recall surveys using a specially developed Ugandan food composition database included in the online tool myfood24. At the same time, we also collected: (i) demographic and contextual data related to Covid-19; (ii) data on weather and seasonality; (iii) data on the perception of dietary intake over the year, and during the Covid-19 period; (iv) baseline anthropometric measurements. The majority of the participants did not achieve nutrient adequacy over the 6 months period, and household dietary diversity scores were generally low. Pregnant and lactating women consumed a diet which was severely inadequate in terms of nutrient consumption. Caloric and nutrient intake varied over the 6 months period, with the highest food consumption in June and lowest in April. Temporal variation was more evident among Batwa participants. Vitamin A intake varied more over months than other nutrients in adults’ and children’s diets, and none met iodine requirements. Participants characterised the diverse mechanisms by which season and weather variability determined the type and amount of food consumed each month. Dietary intake showed indications of temporal variation that differed between nutrients. Also, they reported that the Covid-19 pandemic influenced their diet. During lockdown, 58% of adults reported changing dietary habits by consuming less—and less nutritious—foods. The findings of this work highlight that the majority of the Batwa and Bakiga participants did not meet the dietary requirements for their age and gender. Also, our research indicates that weather patterns and seasonality may cause variations in smallholder food production with consequences on households’ dietary intake. Emerging evidence suggests that nutrients and caloric intake vary monthly and under different weather conditions. Accurate and time-varying nutrition evaluations would help in identifying seasonal and monthly dietary needs, supporting preventive interventions protecting children and their parents from any form of malnutrition. Consideration of time-varying nutritional intake will become increasingly important as climate change affects the seasonality and availability of smallholder subsistence crops.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,171
Score d'incertitude au seuil0,954

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0150,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,114
Tête enseignante GPT0,470
Écart entre enseignants0,356 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2023
Routes d'admission2
Résumé présentoui

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