Financial issues in times of a COVID‐19 pandemic in a tertiary hospital in Mali
Bibliographic record
Abstract
BACKGROUND: This study examines how the functioning of healthcare providers during the COVID-19 pandemic was affected by the government financing response, which was shaped by existing healthcare financing systems. METHODS: The study applied a single case study design at a tertiary hospital in Bamako during the 1st and 2nd waves of the COVID-19 pandemic. Data were gathered through 51 in-depth interviews with hospital staff, participatory observation, and reviewing media articles and hospital financial records. RESULTS: The study revealed the disruptions experienced by hospital managers, human resources for health and patients in Mali during the early stages of the pandemic. While the government aimed to support universal access to COVID-19-related services, efforts were undermined by issues associated with complex public financing management procedures. The hospital experienced long delays in transferring government funds. The hospital suffered a decrease in revenue during the early stages of the pandemic. Government budgets were not effectively used because of complex, non-agile procedures that could not adapt to the emergency. The challenges faced by the hospitals led to the delays in the staff payments of salaries and promised bonuses, which created potential for unfair treatment of patients. Excluding some COVID-19 related items from the government funded benefit package created a financial burden on people receiving services. The managerial challenges experienced in the study hospital during the first wave continued in the second wave. CONCLUSIONS: Pre-existent issues in healthcare financing and governance constrained the effective management of COVID-19-related services and created confusion at the front line of healthcare service delivery.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".