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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| 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".