Supporting Participant Engagement in Cancer Genomics Research in Rare Cancers: A Qualitative Study of Patients, Caregivers, and Advocates
Bibliographic record
Abstract
IntroductionThe purpose of this study was to identify patterns and themes that support participant engagement in patient-partnered cancer genomics research.MethodsThe Osteosarcoma (OS) and Leiomyosarcoma (LMS) Projects of Count Me In allow any patient with OS and LMS in the US and Canada to contribute their health information, tumor samples, and lived experience to an aggregated, public research database. We conducted in-depth interviews with research partners, including patients, caregivers, and advocates, who were purposefully sampled to ensure inclusion of racial and ethnic minorities, those with less than college education, and adolescents (age 12-17). Coding and analysis were conducted by the research team using NVivo to identify themes that support engagement.ResultsTen patients, ten caregivers, and six advocates were interviewed. Seven themes were identified that support participant engagement: (a) motivation, (b) respect, (c) trust, (d) inclusivity, (e) relationship, (f) engagement, and (g) empowerment. Research partners were motivated to serve others, play a part in scientific discovery, and play a role in a novel initiative. Respect was supported through timeliness in communication or follow-up, an appropriate amount of time and information requested, and an acknowledgement that illness may prevent participation. Trust was developed through ensuring adequate privacy/confidentiality safeguards and demonstrating transparency. Inclusivity was demonstrated through showcasing broad representation and mitigating technical barriers. Research partners wanted to feel a relationship with, and engaged and empowered by, researchers. Adolescents reported their parents were more engaged than they were.ConclusionsResearch partners, including patients, caregivers, and advocates, have a strong desire to engage with researchers. We identified seven themes to support engagement. Researchers can optimize their communication and operations to support participant engagement in cancer genomics research.
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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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".