From Research Participants to Video Stars: Engaging Families in End-of-Grant Knowledge Translation
Bibliographic record
Abstract
This article aims to describe an innovative, arts-based approach to knowledge translation (KT) by involving study participants and families in end-of-grant KT. Examples of end-of-grant KT are scarce in the literature and there is a need to better involve families and participants in various stages of health research, including dissemination. Inclusion of families in research needs to extend beyond serving as participants and on advisory boards. Inclusion of families in end-of-grant KT initiatives can provide a very rewarding experience in which they are able to contribute to enhancing the care experience for others. Our unique KT approach, titled “Mindful Matters,” provides an example of how families may be given “first voice” in creating real and relatable KT materials, such as a video.
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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 | 0.060 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".