Assessment of the socioeconomic impact of COVID-19 in Rwanda: Findings from a country-wide community survey
Bibliographic record
Abstract
Abstract The COVID-19 pandemic disrupted socioeconomic situation worldwide, and particularly in Rwanda which was rebuilding its economy in the aftermath of the 1994 Genocide against the Tutsi. Recent studies documented the macro-level socio-economic pandemic impact but the impact on a household’s daily life has been scarcely documented especially in low-and-middle income countries. This work reports a country-wide longitudinal community survey and describes the interplay between multiple factors to assess the socio-economic impact of COVID-19 on the Rwandan population at micro-level (household). The survey was conducted in Rwanda between December 2021 and March 2022 and data used comprised a total of 26,412 response forms received from around 4400 participants surveyed in 6 recurrent bi-weekly phases. This study revealed that the income of 57.7% of respondents has decreased and 15.5% of respondents received support to overcome the consequences. The univariate analysis results indicate that the decrease in income is more seen for females than males. The other most affected group is of daily laborer or small business (77.1%), people living in urban area (63.7%), retired people (66.4%), and people with primary school education level (62.0%). The multivariable findings highlighted that vulnerable groups: income-poor households with low socio-economic categories and females living in rural regions are among the most impacted in terms of food security, electricity, water and transport. The findings from this research will be used by policy makers to design and implement preventive and responsive measures for future pandemics that should be multifactorial and tailored to transversal parameters like gender and residence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".