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Record W4401623065 · doi:10.3389/frfst.2024.1430391

Assessment of dietary intake in children (6–48 months) and mothers (15–49 years) in different farming systems in Kenya using multiple pass 24-h recall

2024· article· en· W4401623065 on OpenAlexfundno aff
H. K. Wakhungu, George Ooko Abong, Caroline Muthike, Josphat Muema, Nyamai Mutono, Geoffrey Omondi, Samuel M. Thumbi, Zipporah Bukania

Bibliographic record

VenueFrontiers in Food Science and Technology · 2024
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersEgg Farmers of Canada
KeywordsAgricultureEnvironmental healthRecallMedicineAgricultural scienceGeographyBiologyPsychology

Abstract

fetched live from OpenAlex

Background Inadequate food intake is the most common cause of malnutrition worldwide. There is paucity of knowledge on the influence of farming systems, a proxy for contextualizing community-based food networks, and sociocultural perspectives necessary for creating impactful nutritional programs and policies for young children from infancy to early childhood in Kenya and their mothers, especially in Kenya. This study sought to evaluate nutrient intakes of young children and their mothers from Pastoral, Agro-pastoral and Mixed farming system in Kenya. Methods Mothers and their children were recruited from households in Narok south as part of the Animal Health Innovation Study. One day multiple pass 24-h dietary recall was completed for a sample of infants 6–11 months, and toddlers aged 12–48 months (n = 161), and women of reproductive age (15–49 years) (n = 161) via face-to-face interviews with the primary caregiver. Nutrient intakes were estimated using CS Dietary Software and compared with the Adequate Intakes, Recommended Dietary Allowance and/or Estimated Average Requirement. Results The mean intake of key nutrients varied across farming systems. Children aged 6–11 months, met the Adequate Intake and Recommended Dietary Allowance levels for protein and Vitamin A. However, deficiencies were noted in thiamine, vitamin C, vitamin B6, selenium, and niacin across different farming systems, with insufficient Iron intake, particularly in pastoral and agro-pastoral systems (3 mg/d vs. 7 mg/d and 5 mg/d vs. 7 mg/d respectively. Folate intake was significantly lower in pastoral and mixed farming systems, with levels below the recommended 100 μg dfe/d. Calcium intake was sufficient across all farming systems, while phosphorus intake was consistently below the AI of 180 mg/d in children aged 12–48 months. In the mixed farming group, intake exceeded the Recommended Nutrient Intake for calcium, while phosphorus intake remained low across all age groups in agro-pastoral and mixed farming systems. Magnesium intake fell below AI levels in all groups (<65AI). Among women of reproductive age (15–49 years), the agro-pastoral group exhibited the highest carbohydrate intake, while the mixed farming group had the highest protein intake (51.07 ± 6.5). Women met vitamin A recommendations, with zinc, iron, and selenium intake felling below the Adequate Intake in all groups. Conclusion While certain nutrients such as protein and vitamin A intake were met in children and mothers, deficiencies were noted in crucial nutrients like iron and folate across various farming systems. These findings underscore the importance of considering local contextual factors when designing nutrition interventions. To address nutritional disparities and improve overall health outcomes and wellbeing for children and mothers in diverse agricultural settings in Kenya, it is important to prioritize an understanding sociocultural contexts and/or regional variations in designing and implementation of targeted interventions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.267
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2024
Admission routes1
Has abstractyes

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