Public Interest Group on Cancer Research: a successful patient–researcher partnership in Newfoundland and Labrador
Bibliographic record
Abstract
BACKGROUND: Partnering with patients and family members affected by cancer is essential for meaningful research, public engagement and outreach, and advocacy activities. OBJECTIVE: Our objective was to create a public interest group through an academic-community partnership focused on cancer research and public engagement. METHODS: A purposeful recruitment process was implemented to ensure a diverse and inclusive group. The group meets virtually and communicates by email. The group's activities focus on identifying the needs, priorities, and interests of cancer-affected individuals in the province; consultations; and designing research projects and public outreach activities together. Comprehensive meeting minutes are kept and used to distill discussion points. The work of the group is disseminated through a variety of channels. RESULTS: The public interest group includes 12 cancer patient and family member representatives, in addition to researchers. Discussions by the interest group identified key themes related to: (1) equity issues and regional disparity in provincial oncocare; (2) information needs; (3) need for patient empowerment and public understanding; and (4) family member and partner needs and experiences. To date, the group has co-designed a cancer research proposal and a public engagement/outreach activity. The group also provides consultations on cancer-related projects/public engagement activities and members act as patient partners in specific research and public engagement proposals. The group evolves over time, and increasingly advocates on behalf of cancer patients and families. Retention and satisfaction of the public partners with group activities have been high. The group's work and findings are disseminated to the Provincial Cancer Care Program, as well as to public and scientific stakeholders through local media, academic conferences and presentations, and a dedicated website. CONCLUSION: Public Interest Group on Cancer Research represents a highly successful patient-researcher partnership in oncology. It designs meaningful and patient-oriented studies and outreach activities in cancer. It also elevates and widely supports cancer patient and family voice.
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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 | 0.030 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".