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Interventions for Improving Young Children’s Dietary Intake through Early Childhood Settings: A Systematic Review

2015· review· en· W1993233723 on OpenAlexvenueno aff
Lucinda Bell, Rebecca K. Golley

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2015
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionEnvironmental healthGerontologyPediatricsPsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.383
Teacher spread0.339 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations32
Published2015
Admission routes1
Has abstractyes

Explore more

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