Interventions for Improving Young Children’s Dietary Intake through Early Childhood Settings: A Systematic Review
Bibliographic record
Abstract
Early childhood settings (ECS) offer a unique opportunity to intervene to improve children’s nutrition. This paper reviews the literature on early childhood setting interventions that aim to improve children’s dietary intake. Environmental and individual determinants of children’s dietary intakes were also investigated. Prospective intervention studies targeting centres, staff, parents/caregivers or children, were reviewed. Methodological quality was assessed. Twenty six studies (14 weak, 12 moderate quality) were included. Interventions were delivered primarily via training workshops and/or written materials. Study findings favoured intervention effectiveness in 23 studies. Improvements were seen in children’s intake for 8 out of 11 studies assessing dietary intake outcomes. Small increases in fruit and vegetable consumption were observed in five studies. Most studies measuring parental or centre food provision observed post-intervention improvements across a number of food groups, including fruit, vegetables, whole grains and sweetened beverages. Significant improvements in child, parent and/or staff knowledge, attitudes or behaviours were observed consistently across studies. For those studies that included a comparison group, these improvements were observed only in the intervention group. ECS interventions can achieve changes in children’s dietary intake and associated socio- environmental- determinants, although the quality of current research limits confidence in study findings. Future intervention development needs to carefully consider the behavioural targets, modifiable determinants and utilise age-appropriate and effective behaviour change theory, in addition to inclusion of dietary intake outcomes.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".