Les personnes déplacées internes au Mali face à la COVID-19 : une étude qualitative
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has exacerbated an already existing security crisis leading to massive population displacements that have been taking place since 2012. Purpose of research: This study aims to explore the representations of internally displaced persons (IDPs) about the existence of COVID-19 and their knowledge about its signs, symptoms, modes of transmission and prevention measures. Methods: The study was qualitative and exploratory. Individual and group interviews were used to collect data from 52 IDPs in six sites in Bamako and Segou. All interviews were recorded and transcribed. Thematic content analysis and N-Vivo software were used. Results: The majority of IDPs believed in the existence of COVID-19 and had good knowledge of the signs, symptoms, modes of transmission and prevention measures against the disease. However, this was not sufficient for the adoption of public health measures. Among those who believed in its existence, some thought that it was a disease of the white and rich. Finally, a group of participants believed in conspiracy theories and claimed that the government and humanitarian organizations were only trying to make money through these campaigns. Conclusion: To our knowledge, this is the first study in Mali to explore IDPs’ beliefs and knowledge about COVID-19. These results could inform policies, strategies, and interventions to combat COVID-19 in IDP sites and in the general population.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".