Low fruit and vegetable consumption is associated with low knowledge of the details of the 5‐a‐day fruit and vegetable message in the<scp>UK</scp>: findings from two cross‐sectional questionnaire studies
Bibliographic record
Abstract
BACKGROUND: This project aimed to understand the details of the 5-a-day fruit and vegetable (FV) message (which foods are included, portion sizes, the need for variety, reasons for consumption) least known by UK consumers, and most associated with low FV consumption. METHODS: Study 1 assessed FV consumption, knowledge of the details of the message, and relationships between these, using a short questionnaire administered face-to-face to an opportunity sample of one large UK city. Study 2 assessed the same variables using a comprehensive postal questionnaire administered across the UK to a representative population sample. RESULTS: Five hundred and seven respondents completed Study 1 and 247 respondents completed Study 2. The majority of individuals in both studies were aware of the 5-a-day message and could recount this correctly. In both studies, however, knowledge of the details of the message was low, and lower knowledge was associated with lower FV consumption. Respondents had lowest knowledge of the details of the message related to portion sizes and the need for variety. However, FV consumption was not independently associated with knowledge of any one aspect of the message. CONCLUSIONS: These findings suggest that, although most of the UK population sampled were aware of the 5-a-day FV message and could recount this correctly, details of the 5-a-day FV message were not well known, and that FV consumption was related to this knowledge. These findings suggest that strategies to increase FV consumption will benefit from increasing UK consumers' knowledge of the details of the 5-a-day FV message.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".