Partnering for impact: best practices for planning in-person academic events with Patient Partners involvement– Lessons learned from Diabetes Action Canada
Bibliographic record
Abstract
Health-related academic events that focus on patient-oriented research should prioritize the needs and interests of those most affected by their outcomes. Diabetes Action Canada (DAC) has hosted six in-person workshops over eight years, bringing together over 100 participants from research, healthcare delivery, government, non-profit organizations, and communities with lived experience of diabetes. This paper outlines key lessons and best practices from Diabetes Action Canada's collaborative approach to workshop co-design with Patient Partners. For the 2024 workshop, a planning committee, largely composed of Patient Partners, played a central role in shaping the agenda. Their contributions ensured active patient participation, addressed power imbalances, fostered inclusivity, and created supportive spaces. Strategies such as co-designed agendas, symbolic markers for patient-led presentations, and facilitated networking sessions effectively enhanced engagement. Evaluations highlighted the importance of equitable participation and multidisciplinary collaboration, emphasizing the scalability of DAC's co-design principles for diverse research and healthcare contexts. These insights provide a foundation for inclusive, impactful, and patient-centered event planning.
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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 | 0.083 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.019 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".