The Role of Mealtimes in Fostering Language Development and Aligning Home and School Learning: Protocol for a Multi-Method Study of Preschool Children in Rural Kenya and Zambia
Bibliographic record
Abstract
BACKGROUND: The association between school and home is fundamental to sustainable education: parents' understanding of the school's priorities and teachers' understanding of their pupils' home environment are both vital for children to remain in school and succeed academically. The relationship between parents and teachers is closest in preschool settings, providing a valuable opportunity to build bridges between home and school. In this protocol paper, we outline our planned methods for identifying beneficial home and school behaviors. OBJECTIVE: Our project aims to identify culture-specific structures and behaviors in home and school settings, which influence the quantity and quality of child-directed speech and identify positive experiences that can help improve children's linguistic development and nutrition. METHODS: Using a mixed methods approach and focusing on early language learning, nutrition, and responsive caregiving, we will video-record and analyze mealtime language and eating behaviors at home and in school, targeting 80 preschool children and their families in rural Kenya and Zambia. In addition, we will assess children's language skills through audio recordings and use questionnaire-based interviews to collect extensive sociodemographic and dietary data. RESULTS: Between the start of our project in January 2020 and the end of December 2021, we had collected complete sets of sociodemographic, observational, and food recall data for 40 children in Kenya and 16 children in Zambia. By the end of May 2022, we had started data collection for an additional 24 children in Zambia and transcribed and coded approximately 85% of the data. By the end of September, 2022, we plan to complete data collection, transcription, and coding for the entire sample of 80 children across both countries. From September 2022 onwards, we will focus on analyzing our language data, and we hope to have results ready for publication in early 2023. By relating children's language outcomes and nutritional intake to the observed mealtime behaviors, we hope to identify practices that increase the quantity and quality of child-directed speech and improve children's nutritional intake. CONCLUSIONS: Good nutrition and the promotion of language learning are key issues in early childhood development. By using a cross-cultural approach, combining a variety of methods, and working closely with stakeholders and policy makers throughout the project, we hope to find and share best practices for improving children's linguistic outcomes and nutrition and lay the foundation for the development of practitioner networks and parent outreach programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36925.
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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 | 0.037 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".