Video As An Effective Knowledge Transfer Tool to Increase Awareness Among Health Workers and Better Manage Dengue Fever Cases.
Bibliographic record
Abstract
Abstract Background context. For a patient with dengue fever, a wrong diagnosis can be fatal. Very few Burkinabé health workers are properly trained to diagnose and treat cases of dengue fever. Recent outbreaks of dengue fever in Burkina Faso, which is also carrying a significant malaria burden, have made updating health workers’ knowledge an urgent matter.Method. Following a trial to determine the most appropriate format, a video was specially developed as a knowledge translation tool to update health workers’ knowledge. We posted a training video online for front-line medical staff. In four months, it was viewed by 2,993 people. We conducted a qualitative evaluation using the theory of planned behaviour. Twenty interviews were conducted with health professionals who had viewed the video. A content analysis was performed.Results. The use of the knowledge contained 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 context was one of urgency.Conclusion. The development of video as a knowledge translation tool is an effective and efficient way to update health workers’ knowledge and influence their practices. Users received the video enthusiastically because of the epidemic context.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".