Fruit and vegetable consumption in older individuals in Northern Ireland: levels and patterns
Bibliographic record
Abstract
Low intakes of fruit and vegetables have previously been reported in the older population of Great Britain, particularly among certain socio-demographic groups. Levels and patterns of consumption in the older population of Northern Ireland, however, remain unknown. A representative sample of 1000 members of the older population of Northern Ireland were contacted by telephone to assess average intake of all fruits and vegetables and various demographic details. Data from 426 individuals (representative of the whole population) reported a mean consumption of 4.0 (sd 1.3) and 4.1 (sd 1.3) portions of fruit and vegetables per weekday and per weekend day respectively. Regression analyses revealed greater consumption on weekdays by females (B 0.53; P < 0.01), younger individuals (B - 0.02; P = 0.01) and those living in less deprived areas (B - 0.01; P = 0.04), and greater consumption at weekends by females (B 0.54; P < 0.01) and younger individuals (B - 0.03; P = 0.01). The amount of fruit and vegetables consumed is slightly higher than that reported in older populations in Great Britain, possibly as a result of differences in farming practices and rural activities, although levels of consumption remain below current recommendations for health. Patterns of consumption are similar across the UK, and suggest that strategies to increase fruit and vegetable consumption should target males, older individuals and those living in more deprived areas.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".