Video as a public health knowledge transfer tool in Burkina Faso: A mixed evaluation comparing three narrative genres
Bibliographic record
Abstract
BACKGROUND: The dengue virus is endemic in many low- and middle-income countries. In Burkina Faso, the proportion of fevers that could be due to dengue is growing. In 2013, a dengue epidemic spread there, followed by other seasonal outbreaks. Dengue is often confused with malaria, and health workers are not trained to distinguish between them. Three training videos using different narrative genres were tested with nursing students from two institutions in Ouagadougou: journalistic, dramatic and animated video. The study aimed to determine if video is an effective knowledge transfer tool, if narrative genre plays a role in knowledge acquisition, and which narrative elements are the most appreciated. METHODOLOGY: A mixed method research design was used. The relative effectiveness of the videos was verified through a quasi-experimental quantitative component with a comparison group and post-test measurements. A qualitative component identified participants' perceptions regarding the three videos. Data were drawn from a knowledge test (n = 482), three focus groups with health professionals' students (n = 46), and individual interviews with health professionals (n = 10). Descriptive statistics and single-factor variance analysis were produced. A thematic analysis was used to analyse qualitative data. PRINCIPAL FINDINGS: Results showed that all three videos led to significant rates of knowledge improvement when compared with the comparison group (p <0.05): 12.31% for the journalistic video, 20.58% for the dramatic video, and 18.91% for the animated video. The dramatic and animated videos produced a significantly higher increase in knowledge than did the journalistic video (with respectively 8.27% (p = 0.003) and 6.59% (p = 0.029) and can be considered equivalent with a difference of 1.68% (p = 0.895). Thematic analysis also revealed that these two videos were considered to be better knowledge transfer tools. Four key aspects are important to consider for a video to be effective: 1) transmitting information in a narrative form, 2) choosing good communicators, 3) creating a visual instrument that reinforces the message and 4) adapting the message to the local context. CONCLUSIONS: Video has proven to be an effective and appreciated knowledge transfer and training tool for health professionals, but the narrative genre of the videos can influence knowledge acquisition. The production of other videos should be considered for training or updating health professionals and their narrative genre taken into consideration. The actual context of constant circulation of new diseases, such as COVID-19, reaffirms the need to train health professionals.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".