The impact of food insecurity on mental health among older adults residing in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
Over the past few years, food insecurity has been increasing globally due to the COVID-19 pandemic, climate change, economic downturns and conflict and a number of other intersecting factors. Older adults residing in low- and middle-income countries are more vulnerable to food insecurity. While the impacts of food insecurity on physical health outcomes have been thoroughly researched, the effect on mental health outcomes remains under-researched, especially among older adults. Hence, this systematic review aims to investigate existing literature to assess how food insecurity impacts the mental health of older persons residing in LMICs. A systematic search of six databases and Google for studies was conducted. The search was limited to studies written in English and published between 2000 to the present. We identified 725 studies, out of which 40 studies were selected for a full-text review and 12 studies were included for a final analysis. The significant finding in all the included studies was that food insecurity is associated with the worsening mental health of older adults. We also found a complex interplay of factors such as gender, age, rural/urban and health conditions associated with the aggravation of several mental health outcomes. The findings of this study illuminate the need for improved food programs to improve food security and, consequently, mental health among older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".