MétaCan
Menu
Back to cohort
Record 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 on 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

Bibliographic record

VenueEnvironmental Research Health · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchDepartment of Health and Social CareInternational Development Research Centre
KeywordsContext (archaeology)IndigenousEnvironmental healthGeographyAnthropometryMedicineDietary diversityDemographyGerontologySocioeconomicsFood securityBiologyEcologyAgriculture

Abstract

fetched live from 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.470
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Research HealthSame topicChild Nutrition and Water AccessFrench-language works237,207