Encouraging diversity in family engagement in research: Reflections on the development of knowledge translation tools
Bibliographic record
Abstract
BACKGROUND: Family engagement in research is crucial to generating relevant, impactful, and meaningful priorities and outcomes. Although there has been increased awareness and value for patient-oriented research, most patient partners in North America are from Western, educated, industrialized, rich and democratic societies. Encouraging underserviced and marginalized populations to join the partnerships is important. This project demonstrates the development of two knowledge translation tools created to encourage diversity in patient-family and researcher partnerships. CASE STUDY: Our diverse cross-Canadian team embodies the family-researcher partnership as it consists of two research personnel from non-Western origins with immigrant experiences, a parent with lived experience, and a project director. All group members have experience in the field of mental health and neurodevelopmental conditions. Four infographics were created: 3 patient-oriented ones (in English, Chinese, and Farsi) and 1 researcher-targeted one. Content for the infographics were generated to address common barriers to patient engagement identified from literature reviews, as well as key concepts discussed during the McMaster University Continuing Education Family Engagement in Research Certificate Course sponsored by CanChild & Kids Brain Health Network. Peer consultations helped to improve the infographics to be more culturally sensitive and appealing. The patient-oriented infographic presents concise bullet points about 5 main topics: (1) what is research, (2) reasons to join, (3) your role, (4) talking to researchers, and (5) how to join. The researcher-targeted infographic presents concise bullet points about 4 topics: 1) why team up with diverse patient partners, (2) ways to partner, (3) how to connect, and (4) talking to diverse partners. CONCLUSION: Infographics were co-designed to encourage diversity in family engagement in research. Lessons learned throughout the project include barriers encountered (e.g., team collaboration considerations, design limitations) and strategies that facilitated the project (e.g., online collaboration platforms). Future directions include translations into other languages, increased dissemination across agencies, and evaluating the effectiveness of the infographic tools.
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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 | 0.304 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".