Risk it for a biscuit: Food safety behaviours and food insecurity of older adults
Bibliographic record
Abstract
Foodborne disease presents a significant public health issue, costing the UK economy £9 billion annually, with many incidences being due to food-related behaviours in the home. Adults aged 60 and over account for around a quarter of the population in England and Wales and are at a greater risk of foodborne disease and may suffer a much higher burden. Research into risky food behaviours has previously focused on larger cohorts and typically treats the over 60's as one homogenous group. The current paper aims to identify the characteristics associated with risky food-related practices related to cooking, cleaning, chilling, cross-contamination, and use-by date adherence. The current research analysed data from the Official Statistics survey, Food and You 2: Wave 6 (2022-23). A series of binary logistic regression models examined the characteristics associated with risky food-related practices. We demonstrate that the characteristics associated with risky behaviours are not uniform, with different factors being associated with specific behaviours. We suggest that risky behaviours cannot be targeted efficiently with a one size fits all approach. This research provides an evidence base for policy makers to target risky food behaviours in this understudied vulnerable group.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".