Social approaches to COVID-19 pandemic response: effectiveness and practicality in sub-Saharan Africa.
Bibliographic record
Abstract
INTRODUCTION: the threat of the coronavirus disease 2019 (COVID-19) pandemic to health systems and communities in sub-Saharan Africa (SSA) is enormous. Social approaches such as distancing measures are essential components of the public health response to respiratory-related infectious disease outbreaks. Due to socio-economic and broader peculiarities of SSA countries, social approaches that were effective elsewhere may have limited practicality in these contexts, and if practical; may yield different or even adverse results. We highlighted the effectiveness of these social approaches and their practicality in SSA. METHODS: we conducted a comprehensive literature search through multiple databases, to identify articles relevant to social distancing. Findings were thematically summarized. RESULTS: our review found emerging and varying empirical evidence on the effectiveness of social approaches in the control and mitigation of the COVID-19 pandemic; thus, limiting its applicability in SSA contexts. Nonetheless, our review demonstrates that the effectiveness and practicality of social approaches in SSA contexts will depend on available resources; timing, duration, and intensity of the intervention; and compliance. Weak political coordination, anti-science sentiments, distrust of political leaders and limited implementation of legal frameworks can also affect practicality. CONCLUSION: to overcome these challenges, tailoring and adaptation of these measures to different but unique contexts for maximum effectiveness, and investment in social insurance mechanisms, are vital.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".