A comparison of eating habits between retired or semi-retired aged subjects and younger subjects in full-time work
Bibliographic record
Abstract
An analysis of eating habits in older retired subjects (“No Work group”) and younger subjects employed in full-time work (“Work group”) has been carried out. It used a questionnaire that assessed why individuals chose to eat or not to eat meals during the course of the day, and subjective responses to the meals. The questionnaire was answered every three hours over a “typical week” which, for the Work group, entailed working during the weekdays and resting at the weekend. For the “No Work” group, breakfast was the most frequently taken meal of the day whereas, for the “Work” group, this meal was often missed. Patterns of meal intake were not significantly different between the weekdays and weekend for the “No Work” group, but the “Work” group ate more hot food at the weekend. The reasons cited for eating/not eating a meal and for choosing the type of meal eaten were dominated by hunger/lack of hunger in both groups. In addition, whereas habits were also important for the “No Work” group, it was time availability or the lack of it that was of major importance to the Work group, though this was significantly less important at the weekend. Meals which required more time for preparation and cooking were appreciated significantly more (appetite before the meal, enjoyment during it, and satiety afterwards) than meals such as snacks and cold food, which could be prepared more quickly. Some implications of these results, with regard to regular meals acting as a social zeitgeber in older subjects and the additional constraints imposed upon night workers, are considered.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".