Humanitarian Food Security Interventions during the COVID-19 Pandemic in Low- and Middle-Income Countries: A Review of Actions among Non-State Actors
Bibliographic record
Abstract
Widespread food insecurity has emerged as a global humanitarian crisis during the coronavirus disease 2019 (COVID-19) pandemic. In response, international non-governmental organizations (INGOs) and United Nations (UN) agencies have mobilized to address the food security needs among different populations. The objective of this review was to identify and describe food security interventions implemented by INGOs and UN agencies during the early stages of the pandemic. Using a rapid review methodology, we reviewed food security interventions implemented by five INGOs and three UN agencies between 31 December 2019 and 31 May 2020. Descriptive statistical and content analyses were used to explore the extent, range, and nature of these interventions. In total, 416 interventions were identified across 107 low- and middle-income countries. Non-state actors have developed new interventions to directly respond to the food security needs created by the pandemic. In addition, these humanitarian organizations have adapted (e.g., new public health protocols, use of technology) and reframed existing initiatives to position their efforts in the context of the pandemic. These findings provide a useful baseline to monitor how non-state actors, in addition to the food security interventions these organizations implement, continue to be influenced by the pandemic. In addition, these findings provide insights into the different ways in which INGOs and UN agencies mobilized resources during the early and uncertain stages of the pandemic.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".