Demographic and Socioeconomic Factors Associated to Fruits and Vegetables Consumption in Elderly Europeans: A Systematic Review
Bibliographic record
Abstract
Several epidemiological studies stress the association between a diet based on high fruits and vegetables intake and a better health condition. However, elderly Europeans cannot manage the recommended fruits and vegetables consumption. This systematic review aims to explore the main factors related to fruits and vegetables consumption in elderly Europeans. We conducted literature searches on Medline, Scopus, and Web of Science from inception to May 2022. Published articles including data related to certain fruits and vegetables consumption among elderly Europeans were selected. The New Castle-Ottawa Scale and National Heart, Lung, and Blood Institute tools were used for methodological quality assessment by two authors independently. A total of 60 articles were retrieved, and data from twenty-one high-quality cross-sectional studies and five moderate-to-high-quality cohort studies, including a total of 109,516 participants, were synthesized. Associated factors mostly analyzed were those relating to demographic and socioeconomic status, such as sex, age, marital status, educational level, and income. However, the findings show a high discrepancy. Some evidence suggests a possible positive association, while other evidence shows an inverse or no association at all. The relationship between demographic and socioeconomic factors with fruits and vegetables consumption is not at all clear. More epidemiological studies with an appropriate design and corresponding statistical methods are required.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".