P55 The COVID-19 pandemic and food security 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. Recent studies have found a worsening of food security in adults over the COVID-19 pandemic. The aim of this review was to systematically examine the evidence analysing the direct and indirect impacts of the COVID-19 pandemic on food insecurity and diet quality in households with children within high-income countries. Methods For this narrative synthesis, an electronic database search was undertaken on EMBASE, Cochrane Library, International Bibliography of Social Science, and Web of Science. We also searched relevant cites for grey literature. Eligible texts included include households with children, with the study being undertaken in an OECD country comparing the outcomes to before the pandemic or another geographical setting. Studies written in English, published from 01/01/2020 were included. Rayyan software was used for the screening process. Systematic reviews and conference abstracts were excluded. Quality assessment of the selected studies were undertaken by two reviewers using the National Institute of Health tool for observational cohort and cross-sectional studies. Results 5,626 records were identified from the electronic database search, after de-duplication 4,851 titles and abstracts were screened. Full text of 46 studies was screened. Sixteen studies were included in this review. Nine were cross-sectional (asked participants to recall their food security of the months previous) and seven cohort studies. Twelve studies were based in the USA, one in Canada, one in Italy and two in the UK. Eight studies were rated of ‘good’ quality, seven were rated of ‘fair’ quality, and one study (grey literature) did not fit to the quality assessment criteria. Thirteen studies reported the COVID-19 pandemic worsened food insecurity in households with children <18 years. Job disruption was found to be a key influencer of food insecurity during the pandemic, and access to existing interventions which support families to afford nutritious food were protective against food insecurity. Around half of participants in three studies investigating diet quality had a change in diet. This was more pronounced in those with food insecurity. Conclusion Although the studies elicited varying results and measured food insecurity using different tools, most showed that the COVID-19 pandemic worsened food security in households with children. Given the study designs, it is difficult to infer causality in this relationship. 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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".