Malaysian Mothers’ Feeding Practices, Household Food Security and Picky Eating Behaviour: Lesson Learnt From Covid-19 Pandemic
Bibliographic record
Abstract
Introduction: Drastic changes in food access during the COVID-19 pandemic may impact families’ feeding practices, and eating behaviour. This study aimed to determine the association between maternal feeding practices, household food security status, and children’s picky eating among Malaysian mothers. Methods: A cross-sectional study was conducted virtually among 102 mothers with children aged from 5 to 12 years old (January-June 2022). Self-administered questionnaires on sociodemographic, feeding practices, household food security and child-picky eating behaviour were used. The statistical associations for key variables were analysed. Results: Most participants were Chinese mothers (87.3%) aged 30-39 years old (52.0%) with tertiary education (52.0%) and from middle to high household income (64.7%). Restriction domain had the highest median (interquartile range) score of 3.63 (1.00). A quarter of the household was experiencing food insecurity and about 34.3% of the children were classified as ‘picky eaters’. A significant difference was found for restrictive feeding practices and household food security status (3.75 (0.97) versus 3.13 (1.19), p=0.022), while mothers of picky eaters were more likely to pressure their children to eat compared to non-picky eaters (3.75 (0.75) versus 3.25 (1.00), p=0.01). Conclusion: Despite food secure status during the pandemic, two food parenting approaches (restriction and pressure to eat) were significantly associated with the household food security status and children’s picky eating behaviour. While the additional environmental stress from the COVID-19 pandemic could be transient, there should be consideration of the long-term impact of maternal feeding practices on the health and nutritional status of growing children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".