The Development and Impact of AYA Can—Canadian Cancer Advocacy: A Peer-Led Advocacy Organization for Adolescent and Young Adult Cancer in Canada
Bibliographic record
Abstract
Adolescents and young adults (AYAs; 15-39 years) diagnosed with cancer face disparities in outcomes and survival. Patient advocacy organizations can play a pivotal role in advancing outcomes for underserved health conditions, such as AYA cancer. In 2018 a group of AYA patient advocates founded AYA Canada (later renamed to "AYA Can-Canadian Cancer Advocacy"), a peer-led national organization aimed at improving the experiences and outcomes of Canadian AYAs affected by cancer. The aim of this article is to describe the development and impact of AYA Can. AYA Can was incorporated as a not-for-profit organization in 2021 and became a registered charity in 2023. Since 2018, AYA Can has established a thriving community of practice comprising nearly 300 patients, healthcare providers, researchers, and charitable organizations with an interest in advocacy for AYA cancer. Other activities have included advocacy at academic conferences and on scientific committees, collaboration with scientists to advance AYA cancer research, training the next generation of AYA patient advocates through a "patient ambassador program," and developing a national resource hub to centralize knowledge and information on AYA cancer. Through its work to foster collaboration and amplify patient priorities on a national scale, AYA Can has become a leading voice for AYA cancer advocacy in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".