Factors contributing to food choice in the UK secondary school food setting: a systems map perspective
Bibliographic record
Abstract
Abstract Objective: To co-develop a systems map of the UK secondary school food system and to understand what factors contribute to food choice within it. Design: Participatory methods were used with a range of UK school stakeholders to co-produce a systems map of factors contributing to food choice in the secondary school food system. An online survey with stakeholders ( n 26) was used to gather an initial list of factors, and a group model-building workshop was conducted with stakeholders ( n 13) to establish relationships between these factors. Two school workshops captured the views of students ( n 17). The map then underwent final refinement by the research team, and all stakeholders were provided the opportunity to provide feedback on the final version. Setting: United Kingdom. Participants: UK school stakeholders. Results: The systems map contained twenty-four factors with forty-three direct causal relationships between them, each factor falling into one of six themes: catering and procurement; school leadership and governance; the priority of food within schools; social experience, behaviours and attitudes; the food space and experience in school and financial. The map demonstrates how each of the factors interacts with each other, including the direction of influence. It also reveals feedback loops that shape and sustain food choice patterns in secondary schools. Conclusions: The systems map provides a visualisation of the complex secondary school food system and can be used by stakeholders in the design and evaluation of whole-school, multi-component interventions and programmes targeting food choice in secondary schools.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".