Designing and delivering public engagement activities together with patient partners: The experience of Public Conference on Cancer
Bibliographic record
Abstract
Abstract Public outreach and engagement activities can greatly benefit from collaborating with patient partners in planning and execution. Here, we describe and reflect on our experience regarding the organization and delivery of the Public Conference on Cancer, a virtual public event that aimed to exchange knowledge on cancer, cancer lived experiences, and cancer services and support in Newfoundland and Labrador, Canada. The Public Interest Group on Cancer Research, including 12 cancer-affected members, four scientists, as well as a clinician and a research assistant formed the conference organizing team. The team's experiences and reflections and the feedback received after the Conference were used to distill perspectives gained, lessons learned, and opportunities identified. The Public Conference on Cancer was a successful public engagement event. It reached out to the general public, initiated new or strengthened existing connections among stakeholders, disseminated important clinical and social knowledge on cancer, and gave us further ideas and expertise to use in future public outreach activities. We also realized challenges remaining, such as the need to improve accessibility and recruitment, and unique considerations for patient speakers and speakers from special/vulnerable communities. While there are considerations that need to be further elaborated and integrated for widely accessible public engagement activities, partnering with cancer patients and family members in designing and delivering public outreach activities is effective in knowledge dissemination, personal and professional growth, and forming connections with stakeholders.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".