Dengue rapid diagnostic tests: Health professionals’ practices and challenges in Burkina Faso
Bibliographic record
Abstract
Objectives: Dengue fever remains unrecognized and under-reported in Africa due to several factors, including health professionals’ lack of awareness, important prevalence of other febrile illnesses, most of which are treated presumptively as malaria, and the absence of surveillance systems. In Burkina Faso, health centers have no diagnostic tools to identify and manage dengue, which remains ignored, despite the evidence of seasonal outbreaks in recent years. A qualitative study was conducted to analyze the use of rapid diagnostic tests in six health and social promotion centers (i.e. health-care centers, from the French Centers de Santé et de Promotion Sociale) of Ouagadougou (Burkina Faso) in an exploratory research context. Methods: Dengue rapid diagnostic tests were introduced into fever-related consultations from December 2013 to January 2014. In-depth individual interviews were conducted in May and June 2014 with 32 health professionals. Results: Prior to the introduction of the tests, dengue was not well known or diagnosed by health professionals during consultations. Most febrile cases were routinely presumed to be malaria and treated accordingly. With training and routine use of rapid diagnostic tests, health professionals became more knowledgeable about dengue, improving the diagnosis of non-malaria febrile cases and its management, and better prescription practices. Conclusions: In a context of dengue re-emergence and high prevalence of other febrile illnesses, having rapid diagnostic tools available, especially during epidemics reinforces health professionals’ diagnostic and prescribing capacities, allowing an opportune and accurate case management and facilitates diseases surveillance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".