The COVID-19 pandemic and food insecurity in households with children: A systematic review
Bibliographic record
Abstract
BACKGROUND: Food insecurity is defined as not having safe and regular access to nutritious food to meet basic needs. This review aimed to systematically examine the evidence analysing the impacts of the COVID-19 pandemic on food insecurity and diet quality in households with children <18 years in high-income countries. METHODS: EMBASE, Cochrane Library, International Bibliography of Social Science, and Web of Science; and relevant sites for grey literature were searched on 01/09/2023. Observational studies published from 01/01/2020 until 31/08/2023 in English were included. Systematic reviews and conference abstracts were excluded. Studies with population from countries in the Organisation for Economic Co-Operation and Development were included. Studies were excluded if their population did not include households with children under 18 years. The National Heart, Lung, and Blood institute (NIH) tool for observational cohort and cross-sectional studies was used for quality assessment. The results are presented as a narrative review. RESULTS: 5,626 records were identified and 19 studies were included. Thirteen were cross-sectional, and six cohorts. Twelve studies were based in the USA, three in Canada, one each in Italy and Australia and two in the UK. Twelve studies reported that the COVID-19 pandemic worsened food insecurity in households with children. One study reported that very low food security had improved likely due to increase in benefits as part of responsive actions to the pandemic by the government. CONCLUSION: Although studies measured food insecurity using different tools, most showed that the pandemic worsened food security in households with children. Lack of diversity in recruited population groups and oversampling of high-risk groups leads to a non-representative sample limiting the generalisability. Food insecure families should be supported, and interventions targeting food insecurity should be developed to improve long-term health.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".