Food Security Interventions among Refugees around the Globe: A Scoping Review
Bibliographic record
Abstract
There are 26 million refugees globally, with as many as 80% facing food insecurity irrespective of location. Food insecurity results in malnutrition beginning at an early age and disproportionately affects certain groups such as women. Food security is a complex issue and must consider gender, policies, social and cultural contexts that refugees face. Our aim is to assess what is known about food security interventions in refugees and identify existing gaps in knowledge. This scoping review followed the guidelines set out in the PRISMA Extension for Scoping Reviews. We included all articles that discussed food security interventions in refugees published between 2010 and 2020. A total of 57 articles were eligible for this study with most interventions providing cash, vouchers, or food transfers; urban agriculture, gardening, animal husbandry, or foraging; nutrition education; and infant and young child feeding. Urban agriculture and nutrition education were more prevalent in destination countries. While urban agriculture was a focus of the FAO and cash/voucher interventions were implemented by the WFP, the level of collaboration between UN agencies was unclear. Food security was directly measured in 39% of studies, half of which used the UN's Food Consumption Score, and the remainder using a variety of methods. As substantiated in the literature, gender considerations are vital to the success of food security interventions, and although studies include this in the planning process, few see gender considerations through to implementation. Including host communities in food security interventions improves the refugee-host relationship. Collaboration should be encouraged among aid organizations. To assess intervention efficacy, food security should be measured with a consistent tool. With the number of refugees in the world continuing to rise, further efforts are required to transition from acute aid to sustainability through livelihood strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 teacher head, 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".