Novel Framework of Financial Hardship in Childhood Cancer: Incorporating Stakeholder Perspectives
Bibliographic record
Abstract
PURPOSE: Although financial hardship in childhood cancer contributes to poor outcomes, no standardized tool to assess its impact exists. Existing methods are heterogeneous and designed using high-income country (HIC), adult perspectives. This project aimed to construct a stakeholder-informed conceptual framework of financial hardship in childhood cancer with global relevancy. METHODS: Group concept mapping, a participatory mixed-methods approach, was used. Participants were parents or caregivers of a child with cancer, childhood cancer survivors, and clinical or nonclinical support personnel, fluent in English, Spanish, or Portuguese. A representative panel established a comprehensive list of relevant items. Participants individually sorted these items into concepts and then rated each item for impact using a four-point Likert scale. Multidimensional scaling and hierarchical cluster analysis identified concepts. Descriptive statistics were calculated for impact ratings. RESULTS: One fourth (21/80) of participants were parents/caregivers or childhood cancer survivors. Participants worked in clinical (44/80), charity/volunteer (27/80), and other nonclinical (13/80) roles. Of the 41 represented countries, 78.0% (32) were low- and middle-income countries (LMICs). Conceptual themes spanned six distinct clusters: medical, nonmedical, assistance and support, treatment impact, family impact, and caregiver impact. These were distinct in composition compared with an existing framework for adult oncology. Caregiver impact (mean, 3.39) and treatment impact (mean, 3.29) were the highest rated clusters, and impact ratings were higher in LMICs compared with HICs. CONCLUSION: We developed a framework for financial hardship in childhood cancer that reflects the voices of stakeholders, including parents and caregivers, from diverse global contexts. The findings lay a foundation for the development and validation of tools to systematically assess financial hardship in families of children with cancer and inform effective policies and interventions.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| 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".