Video as an effective knowledge transfer tool to increase awareness among health workers and better manage dengue fever cases
Bibliographic record
Abstract
Background For a patient with dengue fever, a wrong diagnosis can be fatal. Unfortunately, very few Burkinabé health workers are adequately trained to diagnose and treat cases of dengue fever. Recent outbreaks of dengue fever in Burkina Faso, which carries a significant malaria burden, have made updating health workers’ knowledge urgent. Following a trial to determine the most appropriate format, a video was specially developed as a knowledge translation tool to update health workers’ knowledge. Methods The video was sent to front-line medical staff. Within four months, it was viewed by 2,993 people. A qualitative evaluation was conducted using the Theory of Planned Behaviour. Twenty-one health professionals who viewed the video agreed to participate in interviews on which content analysis was performed. Results The uptake of the knowledge in the video was mainly influenced by the fact that its format was adapted to the target audience, that it presented specific and concise information, that it conveyed a relevant message in everyday language, and that the participants urgently needed the content. Conclusions Video development as a knowledge transfer tool is an effective and efficient way to update health workers’ knowledge and influence their practices. Users received the video enthusiastically due to the epidemic context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".