The Co-Creation of a Psychosocial Support Website for Advanced Cancer Patients Obtaining a Long-Term Response to Immunotherapy or Targeted Therapy
Bibliographic record
Abstract
Due to new treatment options, the number of patients living longer with advanced cancer is rapidly growing. While this is promising, many long-term responders (LTRs) face difficulties adapting to life with cancer due to persistent uncertainty, feeling misunderstood, and insufficient tools to navigate their “new normal”. Using the Person-Based Approach, this study developed and evaluated a website in co-creation with LTRs, healthcare professionals, and service providers, offering evidence-based information and tools for LTRs. We identified the key issues (i.e., living with uncertainty, relationships with close others, mourning losses, and adapting to life with cancer) and established the website’s main goals: acknowledging and normalizing emotions, difficulties, and challenges LTRs face and providing tailored information and practical tools. The prototype was improved through repeated feedback from a user panel (n = 9). In the evaluation phase (n = 43), 68% of participants rated the website’s usability as good or excellent. Interview data indicated that participants experienced recognition through portrait videos and quotes, valued the psycho-education via written text and (animated) videos, and made use of the practical tools (e.g. conversation aid), confirming that the main goals were achieved. Approximately 90% of participants indicated they would recommend the website to other LTRs. The Dutch website—Doorlevenmetkanker (i.e., continuing life with cancer) was officially launched in March 2025 in the Netherlands.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".