Assessment of the socioeconomic impact of COVID-19 in Rwanda: Findings from a country-wide community survey, preliminary analysis to inform further global research
Why this work is in the frame
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Bibliographic record
Abstract
The COVID-19 pandemic along with its devastating impact on human lives has disrupted the socioeconomic situation worldwide. Rwanda has adopted lockdowns and other measures to prevent the spread of the COVID-19 pandemic. 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 describes the interplay between multiple factors to assess the socio-economic impact of COVID-19 on the Rwandan population at the micro-level (household). Data from a country-wide community survey conducted in Rwanda between December 2021 and March 2022 were used. A total of 26,412 response forms were received from around 4400 participants surveyed in 6 recurrent bi-weekly phases where participants were randomly selected. The Multivariable Logistic regression model was fitted to data with a backward stepwise elimination algorithm to assess the socioeconomic impact of COVID-19 on households’ income. Factors considered in this study are gender, age group, residence, level of education, occupation, change in employment status, socioeconomic status, and marital status. The multivariable logistic regression model provided the factors associated with the decline in income due to COVID-19. The results show that people living without a partner are more likely to experience income decline due to COVID-19 than people living with their partner. It is seen that the higher the number of children in a household, the higher the risk of experiencing a decrease in income. Taking into consideration the education level and comparing people with post-secondary and university level vis-a-vis people who did not attend school, the latter are 27 times more likely to experience a decrease in their income, those who attended primary school are 5 times more likely to experience a decrease in income, and those who attended secondary school are almost 2 times more likely to experience a decrease in income. The findings from this research will be used by policymakers and other stakeholders 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it