A Realist Scoping Review of Community Nutrition Interventions in the UK: Implications for the ‘Nutrition Skills for Life’ Programme
Bibliographic record
Abstract
BACKGROUND: Nutrition Skills for Life (NSFL) provides training and support for communities and organisations to implement Community Nutrition Interventions (CNIs) that meet identified needs. To inform future NSFL evaluation, this scoping review, using a realist approach sought to determine the underpinning initial programme theory (IPT) for how CNIs support socioeconomically disadvantaged (SED) communities to access a healthy diet, as detailed in the protocol doi.org/10.17605/OSF.IO/D56FK.OSF.IO/D56FK. METHODOLOGY: Reporting standards for realist syntheses (RAMESES) and scoping reviews (PRISMA-ScR) were used. Four electronic databases and grey literature were searched. Of the 1920 documents identified, 45 were included in the analysis. Data relating to Context, Mechanism and Outcomes were extracted and presented as C-M-O configurations (CMOCs). Documents were assessed for relevance to the research question and usefulness in terms of their contribution towards the IPT. RESULTS: The IPT, underpinned by the Ottawa Charter for Health Promotion, comprises 17 consolidated CMOCs. These are narratively discussed as follows: understanding community needs; consistent nutrition messages; knowledgeable, skilled, confident practitioners/facilitators and practising new skills. CONCLUSIONS: Realist research and analysis of CMOCs provided a deeper understanding of how CNIs can be implemented to support SED communities in accessing a healthy diet. Interventions 'worked' when they acknowledged and addressed identified barriers to healthy eating, provided reliable, trusted, easy-to-understand nutrition messages, were delivered by confident, knowledgeable practitioners, and facilitated strategies such as meal preparation. Further realist evaluation to refine the IPT could inform the evaluation of other complex public health interventions.
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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 | 0.134 | 0.338 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".