Engaging Under‐Represented Adolescents and Young Adults in Cancer Research: A Qualitative Exploration of Lived Experiences and Engagement Strategies
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults (AYAs; 15-39 years) diagnosed with cancer face unique challenges during and after treatment, which have implications for improving cancer care. However, AYA cancer research is limited by the under-representation of those who identify as Indigenous, racialized, 2S/LGBTQIA+, and those living with disabilities. The aim of this project was to explore lived experiences and identify barriers and enablers to engagement, to inform strategies that facilitate the inclusion of under-represented AYAs in cancer research. METHODS: Using a community-based partnership research (CBPR) approach, under-represented AYAs diagnosed with cancer, along with non-patient advocates who serve this population, were recruited through social media, snowball, and convenience sampling. Semi-structured interviews were conducted virtually through Zoom and analyzed using reflexive thematic analysis. Analyses were informed by collaborations with a patient partner and member-checking feedback. RESULTS: Interviews were conducted with AYAs (n = 17) and non-patient advocates (n = 2). Analyses generated three themes: (1) representation leads to empowerment, (2) person-centered approaches are a prerequisite to building connections, and (3) structural contexts influence the impact of research. DISCUSSION: Our findings inform inclusive strategies for future studies to ensure the voices of all AYAs with cancer are represented in research. This study also highlights the importance of CBPR and the positive impact of knowledge derived from lived experiences in shaping research processes and outcomes.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".