Co-Designing a User-Centered Digital Health Tool for Supportive Care Needs of Patients With Brain Tumors and Their Caregivers: Interview Analysis
Bibliographic record
Abstract
Background: Brain tumors are characterized by the high burden of disease that profoundly impacts the quality of life in patients and their families. Digital health tools hold tremendous potential to enhance supportive care and quality of life for patients with brain tumors and their caregivers. Objective: This study aims to generate ideas and concepts, through a co-design paradigm, to inform the development of a digital health tool to address the unmet needs of people affected by brain tumors. Methods: Patients with brain tumors, caregivers, and health professionals from 2 large public tertiary hospitals in Victoria, Australia, were invited to complete a qualitative interview discussing their unmet needs of care. Overall, 35 qualitative interviews focusing on unmet needs and concepts for a digital health tool were conducted with 13 patients, 11 caregivers, and 11 health professionals. Interviews were audio recorded and transcribed, and a 5-step framework analysis approach was used to analyze data. Results: Four themes of unmet supportive care needs emerged: (1) emotional and psychological, (2) information, (3) physical and practical, and (4) social connectedness. Participants expressed the desire for early and proactive mental health intervention, noted the importance of providing mental health support to caregivers, and emphasized the need for positive stories and affirmative language. From an information perspective, participants noted a sense of information overload, especially at the beginning. They also underscored the variety of information needed on an ongoing basis, including life after treatment, and comprehensive care assistance to maintain quality of life. Participants also described unmet supportive care needs relating to symptom burden, and practical and administrative support to facilitate the logistics of accessing treatment and accomplishing daily life tasks. Finally, they expressed the desire for greater social connectedness and safe spaces to engage with other people in a similar situation. Our findings are consistent with previous research on this subject and were integrated into the development of a web-based platform. Conclusions: Participants' perspectives informed the development of content for a web-based digital health platform called "Brain Tumours Online." The platform comprises three pillars-(1) "LEARN": a repository of vetted information about a range of biomedical and psychosocial care topics; (2) "CONNECT": a digital peer support community with a health care professional interface; and (3) "TOOLBOX": an emerging library of validated digital therapeutics for symptom management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".