Toward a public outreach and community engagement strategy on cancer in Newfoundland and Labrador: an initial road map and recommendations
Bibliographic record
Abstract
Abstract Background: The province of Newfoundland and Labrador has one of the highest incidence and mortality rates for cancer among the Canadian provinces. Sharing accessible knowledge on cancer is an important part of cancer control and population health efforts. However, bringing useful health information and knowledge exchange events to residents requires an efficient and accessible system. Objectives: We aimed to create a road map and recommendations for effective public outreach and community engagement on cancer in Newfoundland and Labrador. Methods: We used the following information, experience, or public engagement tools to gather information to inform our work: discussions with the members of the Public Interest Group on Cancer Research, researchers' experiences with recruitment and knowledge translation activities, feedback provided to 2 public conferences delivered (Public Conference on Genetics delivered in 2020 and Public Conference on Cancer delivered in 2022—where the target audience was general public), and 2 public town halls (one in-person, one virtual) and 2 individual consultation sessions with key stakeholders. Information gathered was then summarized. Results: We identified a rich set of cancer-related topics for which to organize public events on cancer. In addition, a large number of public outreach and engagement options were identified, emphasizing the fragmented, inefficient, and resource-intensive nature of public outreach and community engagement efforts in the province. Based on the information collected, we developed an initial road map and recommendations to inform future public engagement activities and strategies. In addition, our group has started to implement the road map for our current and future public engagement activities. Conclusions: We present key cancer-related topics that are of public interest and issues and opportunities for recruitment and delivery of knowledge and events to residents of Newfoundland and Labrador. This information can be useful for researchers, organizations, and the health care system in the province. However, more inclusive consultations, larger collaborations, funding, and systematic data collection are needed to build a province-wide public outreach network on cancer and to identify more comprehensive public engagement options. The work presented here can potentially guide these efforts. Our work is also expected to inspire other provinces, states, and communities to assess their public outreach status and help develop road maps co-led by patients to progress their public engagement efforts in cancer.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".