Motivations, experiences, and aspirations in patient engagement of people living with metastatic cancer
Bibliographic record
Abstract
The objective of this patient-led study was to explore the motivations, experiences, and aspirations of people living with metastatic cancer who volunteer in patient engagement. This qualitative study filled a gap in lived experience research about patient engagement by focusing on an oft ignored population – those living with metastatic cancer. We used a patient-oriented research approach throughout the research cycle from proposal development to data analysis. A Patient Partner helped develop the project proposal. We selected a qualitative descriptive design to best align with our patient-oriented research goals. The first author, a peer researcher with metastatic cancer, conducted semi-structured interviews with seven participants. The interview questions focused on why patients with metastatic cancer volunteered in patient engagement, the experiences and challenges they encountered as volunteers and what they wanted to achieve in their participation. The interviews were transcribed by the interviewer with personal details redacted for confidentiality. Optional member-checking occurred with three participants. After the interviews, two participants joined the research team to participate in data analysis and interpretation of the findings. Thematic analysis was used to identify common themes in the transcribed and redacted participant interviews. The resulting themes were contributing fully, creating a better cancer experience, making meaningful connections, giving back, and struggling with the system. These findings yielded theme-based advice for both patient partners and administrators for creating meaningful patient engagement. Further research led by patient partners could contribute to a more empowered patient engagement program. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".