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Record W4407165977 · doi:10.1186/s12889-025-21628-4

Process evaluation of Project Daire: a food environment intervention that impacted food knowledge, wellbeing and dietary habits of primary school children

2025· article· en· W4407165977 on OpenAlexfundno aff
Sarah Brennan, Fiona Lavelle, Sarah E. Moore, Dilara Olgacher, Amy Junkin, Moira Dean, Michelle C. McKinley, Patrick McCole, Ruth F. Hunter, Laura Dunne, N.E. O’Connell, Christopher T. Elliott, Danielle McCarthy, Jayne V. Woodside

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersMedical Research CouncilNational Institutes of HealthQueen's UniversityQueen's University Belfast
KeywordsPsychological interventionThematic analysisMedicineBiostatisticsContext (archaeology)Qualitative propertyIntervention (counseling)Public healthQualitative researchMedical educationDescriptive statisticsEnvironmental healthGerontologyNursingSociologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Project DAIRE was a randomised-controlled, factorial design trial which aimed to improve children's health-related quality of life, wellbeing, food knowledge and dietary habits via two multi-component interventions: Nourish and Engage. Nourish was an intervention aiming to alter the school food environment, provide food-based experiences and expose pupils to locally produced foods. Engage was an age-appropriate cross-curricular food education intervention incorporating food, agriculture, nutrition science and related careers. The purpose of this study was to conduct a process evaluation to evaluate DAIRE implementation, mechanisms of impact (MOI) and context to elucidate trial results, and inform scalable implementation of the DAIRE approach for successful future rollout. METHODS: The Medical Research Council's (MRC) framework for process evaluation was followed. Formal (questionnaires designed for process evaluation) and informal (researcher records and communications) methods were used to collect quantitative and qualitative data during the DAIRE trial in relation to process evaluation. Quantitative data were analysed using descriptive statistics and qualitative data via thematic analysis to identify key themes. RESULTS: Fifteen schools and 983 pupils (n = 495 6-7 year olds/Year 3 and n = 488 10-11 year olds/Year 7) were recruited for the 6-month DAIRE intervention; a 100% retention rate was observed at the school level and the interventions had a high level of pupil and teacher acceptability. Nourish schools delivered a higher mean dose of intervention elements (61.4%) than Engage (50%) schools but, overall, mixed implementation of both interventions occurred. DAIRE produced change through four key MOI: social learning, experimental learning, interactive engaging content and real-life connections. Lack of time was the main contextual barrier to implementation and lack of financial cost to schools indicated as a potential facilitator. CONCLUSIONS: This process evaluation helped to identify important findings related to implementation, MOI and context. The most effective elements of the interventions which should be maintained include provision of interactive and engaging intervention elements at no financial cost to the school. Findings also identified suggestions for improvement including provision of increased teacher training, support and planning time, content reduction to facilitate easy integration, and implementation across the full academic year. A sustainable funding and resourcing mechanism is required for successful future roll-out across the UK and beyond. TRIAL REGISTRATIONS: The original trial referenced in this process evaluation is registered as follows: National Institute of Health (NIH) U.S. National Library of Medicine Clinical Trials.gov (ID: NCT04277312; retrospectively registered 11th February 2020).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.351
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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