Development of a Video-based Evidence Synthesis Knowledge Translation Resource: Applying a User-Centred Approach
Bibliographic record
Abstract
Abstract Background People of all ages and walks of life are bombarded with health claims from an array of sources. An understanding of evidence synthesis is important for people to make truly informed healthcare decisions. There is an increasing focus on the use of knowledge translation resources within healthcare; however, the development of these resources has often been poorly described or studied. Objectives This study employs a user-centred approach to develop a video animation resource to explain the purpose, use and importance of evidence synthesis to the general public regarding healthcare decision-making. Methods We employed a user-centred approach to developing a spoken animated video that could explain evidence synthesis to a public audience, conducting several cycles of idea generation, prototyping, user-testing, analysis and refinement. Six researchers with expertise in evidence synthesis and knowledge translation resource development gave input on the key messages of the video animation and informed the first draft of the storyboard and script. Seven members of the public provided feedback on this draft through Think-aloud interviews, which we used to develop a video animation prototype. Seven additional members of the public participated in Think-aloud interviews while watching the video prototype. In addition to interviews, participants completed a questionnaire that collected data on perceived usefulness, desirability, clarity and credibility. One experienced patient and public involvement (PPI) advocate also provided feedback on the script and prototype. At the end of each feedback cycle, we assimilated all data and made necessary changes, resulting in a final, rendered version of the animation video. Results Researchers identified the initial key messages for the SAV as 1) the importance of evidence synthesis, 2) what an evidence synthesis is and 3) how evidence synthesis can impact healthcare decision-making. Using guidance and feedback from members of the public, we produced a three-and-a-half-minute video animation that members of the public rated 9/10 for usefulness, 8/10 for desirability, 8/10 for clarity and 9/10 for credibility. The video was uploaded on YouTube ( https://www.youtube.com/watch?v=nZR0xQmZVQg ) and has been viewed over 5500 times to date. Conclusions Employing a user-centred approach, we developed a video animation knowledge translation resource to explain evidence synthesis to the general public that was assessed as useful, desirable and clear by its intended target audience. This study describes the structured and systematic development of this knowledge translation resource and how key stakeholders and end-users informed the final output.
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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 | 0.091 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".