Exploring how social inequalities in health have influenced the design of Mali’s SARS-CoV-2 testing policy: a qualitative study
Bibliographic record
Abstract
In the fight against infectious diseases, social inequalities in health (SIH) are generally forgotten. Mali, already weakened by security and political unrest, has not been spared by the COVID-19 pandemic. Although the country was unprepared, the authorities were quick to implement public health measures, including a SARS-CoV-2 testing programme. This study aimed to understand if and how social inequalities in health were addressed in the design and planning for the national COVID-19 testing policy in Mali. A qualitative survey was conducted between March and April 2021 in Bamako, the capital of Mali. A total of 26 interviews were conducted with key government actors and national and international partners. A document review of national reports and policy documents complemented this data collection. The results demonstrated that the concept of SIH was unclear to the participants and was not a priority. The authorities focused on a symptom-based testing strategy that was publicly available. Participants also mentioned some efforts to reduce inequalities across geographical territories. The reflection and consideration of SIH within COVID-19 interventions was difficult given the governance approach to response efforts. The urgency of the situation, the perceptions of COVID-19 and the country's pre-existing fragility were factors limiting this reflection. Over time, little action has been taken to adapt to the specific needs of certain groups in the Malian population. This study (re)highlights the need to consider SIH in the planning stages of a public health intervention, to adapt its implementation and to limit the negative impact on SIH.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".