Reshaping Adolescent and Young Adult Cancer Care and Support Through Participatory Engagement
Bibliographic record
Abstract
It is well established that AYAs, defined as individuals between 15–39 years of age, require specific oncologic and supportive care distinct from children and older adults. Yet cancer care and support systems often do not meet the unique needs of AYAs. In British Columbia (BC), approximately 1050 AYAs are diagnosed with cancer each year. There is currently limited provincial AYA cancer care and support programming. In 2022, the Anew Research Collaborative and BC Cancer, met with over 70 AYAs and cancer care allies (health care providers, clinicians, researchers, and supporters) and facilitated a patient-oriented, Participatory Action Research session to reimagine AYA cancer care and support in BC. In this article we discuss our participatory research process and subsequent advances and future opportunities to create a provincial AYA cancer care and support program in BC. We discuss key factors to consider when engaging AYAs to transform cancer care and support systems including considering the care and support needs of AYAs, using participatory methods to support collaboration and disrupt power structures, and orienting the process and outcomes towards action and/or change. Additionally, we reinforce the importance of providing counselling supports when working within difficult or traumatic contexts and the need to engage AYAs from underrepresented groups. Using a participatory, patient-oriented research approach, this project lays the foundation for meaningful engagement of AYAs, alongside cancer care allies, to create tangible changes in AYA cancer care and support that respond the unique experiences, needs and priorities of AYAs; and the approaches used are applicable across many health care contexts.
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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 | 0.052 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".