Social protection programmes in mitigating the socio-economic impacts of the Covid-19 pandemic: a comparative study of Ghana, Kenya, and South Africa
Bibliographic record
Abstract
COVID-19 has become one of the most significant global health crises in history, with a wide range of socio-economic consequences due to the measures taken to stop the spread of the virus. The socio-economic implications of the quarantine caused by COVID-19 have affected all continents. The purpose of the article is to analyze the socio-economic consequences of the quarantine due to the COVID-19 pandemic in Ghana, Kenya and the Republic of South Africa, as well as to examine the critical social protection policy measures taken by the governments of these countries to reduce the vulnerability associated with pandemic prevention measures. This study used content analysis, which allows for the identification of recurring themes, ideas and terminology in the studied database. Directive documents on social protection programs during the pandemic, scientific publications, and reports of international institutions and organizations served as the source of primary information. Based on the content analysis results, 40 documents were selected that met the inclusion criteria: 14 works from Ghana, 13 from Kenya, and 14 from the Republic of South Africa. To investigate the effects of the lockdown caused by COVID-19, content analysis was chosen to identify recurring themes, ideas and terminology in qualitative data collection. A systematic review shows that lockdown measures implemented by the governments of Ghana, Kenya and the Republic of South Africa to mitigate the spread of COVID-19 have led to increased poverty and inequality, lost incomes, worsening food insecurity and increased unemployment. Content analysis found that the impact of COVID-19 differs significantly for men and women, with women experiencing more excellent destructive effects compared to men. The COVID-19 pandemic has harmed rural residents, with poverty rates rising at higher rates and their well-being declining compared to local residents. To respond to the socio-economic consequences of the quarantine due to COVID-19, the countries studied continued existing or introduced new social protection programs to support their citizens. These include cash transfers, food transfers, utility subsidies and fee waivers, community service programs, tax credits, and unemployment benefits. These welfare programs had different parameters consisting of benefits, rights and beneficiaries. Although this study cannot determine the impact of social programs, future studies will be able to assess their impact and effectiveness on beneficiaries.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".