Willingness to comply with physical distancing measures against COVID-19 in four African countries
Bibliographic record
Abstract
### Summary box The world is facing an unprecedented crisis related to the COVID-19 pandemic with many unknowns, which has led to much confusion and anxiety.1 Public health measures have for centuries been the cornerstone of the response to epidemics.2 Among them, physical distancing measures aim to reduce contact between infected and uninfected people.3 As part of the global COVID-19 response, they have been widely used to slow down the spread of the virus in several countries. Despite their overall acceptance, they have been poorly documented, particularly in Africa, and debates persist on their appropriateness and practicality in the context of low-income countries. Many political, ethical and socioeconomic questions arise.4 This article describes the implementation of these measures in four West-African countries—Mali, Burkina Faso, Senegal and Guinea—and discusses people’s willingness to comply with them. We draw on our experiences in crisis management through a collaborative project known as ‘COVID-19 19 en Afrique Francophone’.5 The countries participating in this project were selected on the basis of a call for applications as part of an initiative by the Francophone Africa and Fragility Network, which brings together more than 100 national and international experts.6 In terms of population, Senegal has the highest number of cases, deaths and tested peoples. It is followed by Guinea concerning the number of cases. Burkina Faso …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".