Podcasting and Blogging as Tools to Engage with the Public on the Topic of Cancer: Experience and Perspectives of the Public Interest Group on Cancer Research
Bibliographic record
Abstract
We (Public Interest Group on Cancer Research) started a podcast and guest blog series on cancer in 2024. Our objective in this Commentary is to describe our experience with this series, insights gained, adjustments made to our approach, and our recommendations for future series. Our group identified and invited guests to contribute a blog or podcast episode on cancer, lived experience of cancer, cancer care and research, or advocacy. The podcast episodes were recorded using the WebEx platform (version 45.9.0.33069) and edited using the Kdenlive software (version 23.08.4). The blogs and podcasts were edited, finalized, and posted online for public access. In this manuscript, we utilized descriptive statistics to define and summarize information about the podcast episodes, guest blogs, and categorical responses to guest feedback survey questions, while we presented the responses to open-ended survey questions as quotes and summaries. As a result, during the period of January 2024-July 2025, we aired 28 podcast episodes and 13 guest blogs involving 36 guests. Guests included people from various backgrounds (such as people with lived experience, advocates, scientists, and healthcare providers) and members of equity-deserving communities (such as women, Indigenous and 2SLGBTQIA+ communities). We contemplated and learned as we proceeded with this project and implemented changes to address the issues that arose. In most cases the guests had positive experiences; however, in rare cases, university practices or federal policies prevented guest compensation, creating an unusual barrier. In conclusion, podcasting and blogging are practical public engagement instruments that provide space for sharing messages and knowledge to communicate with members of the public. Systematic barriers, such as policies that hamper guest compensation, need to be fixed for equitable participation, compensation, and engagement. As there is an increased interest in public engagement and knowledge mobilization activities, our learnings shared in this commentary may help other groups initiate or improve their public engagement practices.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".