Development of a video-based evidence synthesis knowledge translation resource: Drawing on a user-centred design approach
Bibliographic record
Abstract
Objectives: We aimed to develop a video animation knowledge translation (KT) resource to explain the purpose, use and importance of evidence synthesis to the public regarding healthcare decision-making. Methods: We drew on a user-centred design approach to develop a spoken animated video (SAV) by conducting two cycles of idea generation, prototyping, user testing, analysis, and refinement. Six researchers identified the initial key messages of the SAV and informed the first draft of the storyboard and script. Seven members of the public provided input on this draft and the key messages through think-aloud interviews, which we used to develop an SAV prototype. Seven additional members of the public participated in think-aloud interviews while watching the video prototype. All members of the public also completed a questionnaire on perceived usefulness, desirability, clarity and credibility. We subsequently synthesised all data to develop the final SAV. Results: Researchers identified the initial key messages as 1) the importance of evidence synthesis, 2) what an evidence synthesis is and 3) how evidence synthesis can impact healthcare decision-making. Members of the public rated the initial video prototype as 9/10 for usefulness, 8/10 for desirability, 8/10 for clarity and 9/10 for credibility. Using their guidance and feedback, we produced a three-and-a-half-minute video animation. The video was uploaded on YouTube, has since been translated into two languages, and viewed over 12,000 times to date. Conclusions: Drawing on user-centred design methods provided a structured and transparent approach to the development of our SAV. Involving members of the public enhanced the credibility and usefulness of the resource. Future work could explore involving the public from the outset to identify key messages in developing KT resources explaining methodological topics. This study describes the systematic development of a KT resource with limited resources and provides transferrable learnings for others wishing to do similar.
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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 | 0.102 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".