Impact of the COVID-19 pandemic on the functioning of front-line health services in the Kati health district in Mali, West Africa: A qualitative study
Bibliographic record
Abstract
Front-line health services (FHSs) are the gateway to health systems. FHSs in Africa have been hit hard by the COVID-19 pandemic. In Mali, FHSs are provided by community health centres ( Centres de Santé Communautaires (CSComs)). The objective of this study, which, to our knowledge, is the first of its kind in Mali, was to assess the impact of the COVID-19 pandemic on the functioning of CSComs within a health district. This qualitative case study was carried out in four CSComs in the Kati Health District in Mali. A three-dimensional analytical framework was designed and used. Data was collected from 24 key informants through semi-structured interviews. Thematic content analysis was performed, and Nvivo software was used. Data analysis showed that the COVID-19 pandemic impacted all dimensions of our analytical framework. Within the CSComs, the following were particularly impacted: 1) the management of activities with adaptations in the management of human and financial resources, infrastructure and equipment, the supply of inputs and medicines and the national health information system/surveillance; 2) the provision of curative, preventive and promotional health services; and 3) the interactions among stakeholders with little coordination of their actions. This study offers insights into how to improve FHSs' resilience to crises. The results indicated dysfunction in routine health services, a decline in patients' use of them, and inadequate coordination among stakeholders. Despite their low level of preparedness, the CSComs were able to ensure continuity of care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".