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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".