A Qualitative Study of the Experience of COVID-19 Patients in Burkina Faso
Bibliographic record
Abstract
In Burkina Faso, the health system is characterized by systemic insufficient and antiquated health-care infrastructures. Consequently, few health-care establishments have the required resources to diagnose and manage patients with COVID-19, and fewer still have intensive care facilities for severely ill patients with COVID. Furthermore, there is a widespread scarcity of qualified health-care staff. The aim of this study was to explore the experiences of patients with COVID-19 who recovered after being cared for in Bobo Dioulasso and Ouagadougou. Using individual semistructured interviews, we performed a cross-sectional qualitative, descriptive study from June 12 to 30, 2020 with the aid of 13 well-educated patients who had survived COVID-19. The results reveal that prior to hospital admission, the main reason that prompted patients to seek care was onset of symptoms of COVID-19, regardless of whether they had been in contact with suspected or confirmed cases. Transmission was mainly believed to have occurred in the community, in the hospital, and during travel. Patient management was punctuated by frequent self-medication with medicinal plants or pharmaceutical drugs. The participants reported a negative perception of hospitalization or home-based management, with several forms of stigmatization, but a positive perception influenced by the satisfactory quality of management in health-care centers. This report of patient experiences could be helpful in improving the management of COVID-19 in Burkina Faso, both in the health-care setting and in home-based 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".