Food Insecurity and Dietary Intake Among Elderly Population: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: This review seeks to determine the relationship between food insecurity among elderly people over the past decades and nutrient deficiency, which is rather unclear. We aim to systematically review the relationship between food insecurity and dietary intake among elderly population. METHODS: In this systematic review, we systematically searched the international databases including PubMed, Web of Sciences, and Scopus for scientifically related papers which have been published up until January 2018. For a more refined search, we used the Medical Subject Headings (MeSH) terms and Emtree. In terms of search protocol, no restrictions were placed on time and language. Two independent reviewers conducted the data refining processes. Validated form (PRISMA) was used to conduct quality assessment and data extraction. RESULTS: Eight cross sectional studies have been included in this review. Two of the studies were conducted in Asia and the remaining six studies were largely based in the United States and Canada. Food insecurity was associated with low levels of vitamin and mineral intakes such as vitamins E, A, B, and D and also zinc, calcium, magnesium, and iron. Most studies also reported insufficient energy, and micro and macronutrients intake among elderly people. CONCLUSIONS: The findings of this review evidence a considerable amount of food insecurity and nutrient deficiency, including vitamins E, C, D, B 2, and B 12 and zinc, phosphorus, and calcium among elderly population. These findings could be used as reliable evidence by policy makers and future complementary analyses.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".