Rural vulnerability and institutional dynamics in the context of COVID-19: A scoping review
Bibliographic record
Abstract
This study reviewed the impact of the coronavirus disease 2019 (COVID-19) on pre-existing vulnerabilities in rural communities using the scoping review strategy. It focused on manuscripts published on the topic in 2020. Based on 39 studies that met our inclusion criteria (out of 507 studies), we note that COVID-19 is exacerbating pre-existing rural vulnerabilities, including poverty, remoteness, socio-economic marginalisation and high unemployment. There is limited evidence that rural communities are resilient to the pandemic. Reduction in household expenditures and the community food system are the only reported forms of resilience. Although local institutions are supporting rural communities in responding to the impacts of the pandemic, several institutional dynamics undermine the effectiveness of the response. The increased risk of the pandemic is likely to reduce incomes and standards of living amongst poor communities. Thus, coping strategies were identified such as starting small gardens in communities, diet changes, targeting community markets with produce rather than retailers and food swap using social media, with food swap being the most adopted coping strategy. Although this study does not offer a comprehensive picture of the levels and nature of vulnerability, resilience and institutional dynamics of rural communities in different parts of the world reveal the limitations of existing knowledge of the vulnerability of rural communities in the context of COVID-19. This underscores the importance of further studies on rural vulnerability in the context of COVID-19 that will enable evidence-based responses to the pandemic in rural contexts.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".