Co-Designing Evidence-Based Videos in Health Care: A Case Exemplar of Developing Creative Knowledge Translation “Evidence-Experience” Resources
Bibliographic record
Abstract
Objective: Well-designed evidence-based resources that reflect participant experiences and priorities are imperative for informed consumer health decision-making and to combat the pervasive health misinformation existing today. Qualitative research data can inform the development of such resources, but the process of reconciling qualitative research data with other sources of evidence through co-design processes is not well described in the literature. In response to the need for such evidence-based materials and corresponding methodological guidance, we co-designed a series of video resources through transdisciplinary and community partnership. In this manuscript, we provide methodological insight into the process of collaborative co-design to improve the utilization of qualitative research evidence into evidence-based resources for the public. Methods: Following from a large qualitative research study, we engaged in a collaborative and creative co-design process involving a multi-stakeholder advisory group guided by Boyd’s co-design framework. We explicate this process, drawing from a case exemplar of transdisciplinary frailty research. Results: We utilized thematic qualitative data to co-produce: (i) an animation, (ii) a documentary-style video, (iii) a video vignette with key messages embedded in narratives of older adults, and (iv) a key-message video delivered by academic health researchers and clinicians. Discussion: The integration of experiential evidence of health care consumers with other sources of research evidence through co-design is an epistemological and procedural challenge with potential to improve public awareness, knowledge, and to support evidence-based decision making.
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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 | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".