A qualitative study on the involvement of adolescents and young adults (AYAs) with cancer during multiple research phases: “plan, structure, and discuss”
Bibliographic record
Abstract
BACKGROUND: Including the lived experience of patients in research is important to improve the quality and outcomes of cancer studies. It is challenging to include adolescents and young adults (AYAs) cancer patients in studies and this accounts even more for AYAs with an uncertain and/or poor prognosis (UPCP). Little is known about involving these AYAs in scientific research. However, by including their lived experiences during multiple phases of research, the quality of the study improves and therefore also the healthcare and quality of life of this unique patient group. We first aimed to document experiences of AYAs and researchers with AYA involvement initiatives using the Involvement Matrix and the nine phases of the research cycle. Second, we aimed to map the (expected) challenges and recommendations, according to patients and researchers, for AYA involvement in each research phase. METHODS: Thirteen semi-structured qualitative interviews were conducted with AYAs and researchers from February 2020 to May 2020. A thematic analysis codebook with a critical realistic framework was used to analyze the data. RESULTS: AYAs and researchers were predominantly positive about AYA involvement within six of the nine phases of research: identify and prioritize topics, develop study design, disseminate information, implement, and evaluate findings. Not all respondents were positive about AYA involvement in the following three phases: formulate research questions, conduct research, and analysis and interpretation. However, few respondents had experience with AYA-researcher collaborations in multiple phases of the research cycle. Last, the results indicate the importance of adding a role (practical support) and two phases (grant application and recruitment) to the Involvement Matrix. CONCLUSION: Our results show the added value of AYA (with a UPCP) involvement within scientific research projects. We recommend researchers to actively think about the level and phase of collaboration prior to each research project, by involving and brainstorming with AYAs at the conception and throughout research projects. Besides, to enhance fruitful participation, we suggest thoroughly discussing the pros and cons of collaboration for each phase together with AYAs via the proposed Involvement Matrix to support transparency. We recommend to report experiences, choices, and results of AYA involvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".