Exploring the implications of COVID-19 on widening health inequalities and the emergence of nutrition insecurity through the lens of organisations involved with the emergency food response
Bibliographic record
Abstract
BACKGROUND: This paper describes the impact of COVID-19 during the first month of containment measures on organisations involved in the emergency food response in one region of the UK and the emerging nutrition insecurity. This is more than eradicating hunger but considers availability of support and health services and the availability of appropriate foods to meet individual requirements. In particular, this paper considers those in rural communities, from lower socioeconomic groups or underlying health conditions. METHODS: Semistructured professional conversations informed the development of a questionnaire which gathered insights from five organisations involved with the emergency food response in the South East, England, UK. Descriptive themes were derived though inductive analysis and are further discussed in relation to UK government food support measures and early published data. RESULTS: Four themes emerged from conversations, including: (1) increasing demand, (2) meeting the needs of specific groups, (3) awareness of food supply and value of supporting local and (4) concerns over sustainability. All organisations mentioned changes in practice and increased demand for emergency food solutions. Positive, rapid and innovative changes helped organisations to adapt to containment restrictions and to meet the needs of vulnerable people. Although concern was raised with regards to meeting the specific needs of those with underlying health conditions and the sustainability of current efforts. CONCLUSION: Considerable gaps in food provision were identified, as well as concerns regarding increased long-term food and nutrition insecurity. The paper makes recommendations to improve nutrition security for the future and considers the lessons learnt from the COVID-19 pandemic. The generalisability of these early insights is unknown but these real-time snapshops can help to direct further research and evaluation.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.052 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".