How do nutrition interventions targeting parents with young children address parental food literacy? A systematic review
Bibliographic record
Abstract
A broad range of community and public health nutrition interventions exist to improve family diet quality. They vary in the approaches taken to address the knowledge, skills and behaviours required to enhance diet quality, also known as food literacy. Little is known about if or how nutrition interventions utilise strategies aligned with a food literacy definition or frameworks as a mechanism for behaviour change. This systematic review synthesises literature on nutrition interventions aimed at parents of young children, using the most widely cited food literacy framework encompassing four domains (plan and manage, select, prepare, eat) to identify gaps and opportunities to guide intervention strategy development. Medline, Embase and CENTRAL were searched for articles published between 2014 and 2024. Randomised and non-randomised nutrition interventions targeting parents of children aged 2-12 years incorporating strategies that align with any of the four food literacy domains) were included. The McGill Mixed Method Appraisal Tool was used for quality assessment, and intervention content was mapped to the four food literacy domains. Of the 3650 articles screened, 37 studies (46 articles) were included. Most were conducted in Western countries (97 %), used randomised design (62 %), and were underpinned by theory (76 %). Twenty-five studies (68 %) included strategies aligned with all four food literacy domains. All studies incorporated strategies within the 'eat' domain, with the majority also addressing 'plan and manage' (89 %), 'prepare' (86 %) and 'select' (76 %). Substantial variation exists in how food literacy is incorporated and/or reported within nutrition interventions targeting parents of young children. This highlights gaps and opportunities for enhancing intervention design, most importantly the application of a food literacy framework to guide the integration of appropriate strategies to enable behaviour change and support improvements in family dietary quality.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".